This paper proposes a fuzzy optimal tracking control scheme for networked nonlinear Markovian switching systems under an event-triggered mechanism. An augmented system is constructed by integrating system dynamics with the reference signal. Then, an actor-criticQ-learning method achieves adaptive control without requiring prior knowledge of system matrices or transition probabilities. An event-triggered mechanism is introduced to update control actions on demand, significantly reducing network communication load while preserving tracking performance. An online actor-critic neural network algorithm is designed. The critic network approximates the value function, and the actor network updates the control policy in real time, enabling collaborative optimal tracking control. Theoretical analysis shows that the resulting neural network-driven closed-loop system exhibits bounded weight estimation errors and is uniformly ultimately bounded. Simulations performed on a tunnel diode circuit model confirm the effectiveness and resource efficiency of the proposed method in a networked environment.

This paper investigates a novel learning-based sliding mode control (SMC) strategy for a class of nonhomogeneous semi-Markov switching systems with incomplete sojourn-time information. In contrast to existing studies, the sojourn-time probability density functions of semi-Markov kernel are considered to be incompletely known, relaxing the assumption of completely known sojourn-time distributions. The main novelty lies in constructing a novel learning-based SMC scheme to realize the attainment of the quasi-sliding mode, overcoming the difficulty caused by incomplete sojourn-time information. By utilizing the upper bounds of sojourn time for each subsystem mode and constructing the multiple-Lyapunov function, sufficient conditions for mean-square stability are derived for the resulting closed-loop system. Moreover, a recursive sliding mode learning controller is developed to ensure finite-time reachability of the sliding motion and to effectively suppress both parametric uncertainties and switching-induced chattering. The effectiveness and practical applicability of the proposed method are validated through the DC-DC buck converter application.

This paper investigates resilient tracking control for multi-agent systems subject to multi-channel asynchronous denial-of-service (DoS) attacks within a directed graph. We consider both homogeneous dynamics and uncertain heterogeneous dynamics in our analysis. We first propose a distributed resilient tracking control strategy against multi-channel asynchronous DoS attacks for homogeneous systems. The concept of equivalent decay-rates across different attacked channels is introduced to derive sufficient conditions for secure tracking control. Building upon this foundation, we extend the method to address tracking control under uncertain heterogeneous dynamics and constrained communication resources. To this end, we propose an event-triggered resilient control strategy based on equivalent decayrate analysis. The strategy reduces communication overhead by adopting demand-driven scheduling and enhances resilience through a decay-rate-guided feedback mechanism. Our proposed algorithms both mitigate asynchronous DoS attacks by constraining attack surfaces and ensure secure tracking under resource constraints. Finally, numerical examples are provided to verify our theoretical analysis.

This paper investigated the stability andH∞performance of discrete-time Markov jump systems (MJSs) with complex transition probabilities (C-TPs) and actuator saturation. The asynchronous phenomenon between the controller mode and system mode transmission, along with actuator saturation, complicates the control design significantly. To overcome this issue, an event-triggered asynchronous controller embedded with a Hidden Markov Model (HMM) was proposed. First, using Lyapunov functional technique, sufficient conditions for the stochastic stability of the closed-loop MJSs under a givenH∞performance index were established. Second, we consider that C-TPs exist in two processes of the HMM, enhancing the practicality of the theoretical results. Furthermore, two slack matrices were introduced, and matrix augmentation method was employed to address the coupling between variables, leading to the derivation of the required controller gains. Finally, Two examples were provided as application examples to validate the effectiveness of the proposed control method.

This paper addresses the asynchronous eventtriggeredH∞control problem for Markov jump systems under multiple constraints. The transition probabilities are assumed to be time-varying and nonhomogeneous. These dynamically varying transition probabilities, governed by a higher-level set of homogeneous Markov transition probabilities. To capture the mismatch phenomenon arising between the system mode and the transition probability mode, a dual hidden Markov model is introduced. By employing the acknowledgment character technique and a denial-of-Service (DoS) interval partitioning method, this paper establishes a mode-dependent switching-like event-triggered scheme to conserve communication resources, while mitigating the adverse effects induced by DoS attacks on the system. Sufficient conditions are derived to guarantee the stochastic stability andH∞performance of the system under DoS attacks. Finally, an economic model is provided to demonstrate the effectiveness of the proposed approach.

This paper investigates secure formation control for unknown networked multi-vehicle systems under denial-of-service (DoS) attacks. Unlike most existing studies that assume a unified attack model across all inter-vehicle communication channels, we propose an asynchronous, distributed DoS attack strategy targeting individual communication links. Specifically, we develop a resilient distributed observer capable of withstanding multi-channel asynchronous DoS attacks. This observer simultaneously provides both specific secure state estimation and output tracking references for each vehicle by introducing the concept of channel-dependent decay rates. Building upon the estimated information, we introduce a novel hybrid policy learning algorithm that combines off-policy and on-policy learning mechanisms. This hybrid approach enables data-driven derivation of decentralized formation control policies while overcoming key limitations of traditional methods, including the requirement for initial stabilizing policies and limitations in dynamic optimization capabilities. Finally, numerical simulations of networked multi-vehicle systems demonstrate the effectiveness of our proposed methodology.

This article considers the output regulation problem for an unknown discrete-time system subject to the random combination of denial-of-service, replay, and deception attacks on both sensor-controller and controller-actuator channels. We propose a learning-based receding-horizon control with historical output signals. It offers two advantages over state and output feedback regulators in the sense that it requires neither exact knowledge of system dynamics nor a direct measurement of external disturbance on one hand, and on the other hand, it can counteract the adverse impact of hybrid attacks on the executive capability of the actuator, regardless of the seriously tampered data on the sensor-controller channel. To overcome technical difficulties from hybrid attacks on both channels, we generalize the Markov-parameter-based time-series control method to generate a data packet containing the current and future control inputs, which are further compromised on the controller-actuator channel. Thus, a recovery procedure is additionally designed to solve the model-free output regulation problem by distinguishing the undamaged predicted inputs based on the proposed hybrid attack detection procedure.

This article presents an adaptive neural network (AdNN)-based fault detection framework for the thermal processes of lithium-ion (Li-ion) batteries governed by 2-D semilinear partial differential equations (PDEs) with partially-known dynamics. To address the challenges of unknown nonlinear heat generation and limited sensor measurements, a two-stage approach combining reduced-order modeling with adaptive neural observation is proposed. First, a computationally tractable reduced-order model is derived through spectral approximation techniques. An adaptive neural observer is then designed to simultaneously estimate battery states and unknown nonlinear dynamics using only available surface temperature measurements. For robust fault detection, a hybrid scheme is developed that integrates model-based residual generation with data-driven threshold generation. Experimental validation on a pouch-type battery demonstrates the effectiveness of the proposed method in reliably detecting thermal abnormalities.

In this article, the double-layered iterative learning control (DLILC) approach is adopted to investigate the tracking control problem of repetitive nonlinear multiple-input-multiple-output (MIMO) systems under false data injection (FDI) attacks. Based on historical data, two control loops in the scheme are devised to improve tracking accuracy. More specifically, an outer loop adaptive set-point tuning mechanism is developed, which is independent of the inner-loop controller. Such a mechanism dynamically optimizes learning gains by leveraging historical data and significantly reduces reliance on preset system parameters. In the inner loop, a proportional-derivative controller is employed to form the feedback circuit. Furthermore, the double dynamic linearization technique is adopted to transform complex nonlinearities, coupling effects, and unknown uncertainties into a set of linearly estimable parameters. To address FDI attacks, an output observer-based real-time compensator is constructed, which is capable of promptly mitigating the impact of such attacks on system outputs. Simulation results demonstrate that the proposed scheme ensures high-precision tracking, substantially reduces computational burden, and exhibits superior resilience against attacks. The approach thus provides a new pathway toward secure and efficient iterative learning control of nonlinear systems.

In this study, the observer-based asynchronous stabilization is addressed for networked system under multichannel attacks, in which the asynchronous phenomenon refers to the mismatch between the controller mode and the actual attack mode. To accurately depict complex attack behaviors, a piecewise homogeneous semi-Markov chain (SMC) model modulated by a superstratum Markov chain is introduced, which can simultaneously describe the randomness of attack mode transitions and the time-varying nature of transition probabilities. Considering that the actual attack modes are inaccessible, an observer-based mode switching delay technique is designed to solve this challenge. Under the framework of a piecewise homogeneous SMC, a sufficient criterion is established to ensure the $\varsigma $ -error mean-square stability under random multichannel denial-of-service attacks by means of a Lyapunov function depending on observed attack modes, piecewise homogeneous variables, and elapsed time. Moreover, matrix decoupling and convexification techniques are employed to reduce the computational complexity. Finally, the effectiveness of the proposed method is demonstrated through two practical simulation cases.

In this paper, we consider the predefined-time formation control problems subjected to various constraints for heterogeneous autonomous systems. We propose a novel distributed predefined-time formation control framework, which consists of a multi-agent system (MAS) estimator layer and a local tracking controller layer. Firstly, the MAS estimator layer is designed to tackle predefined-time distributed formation optimization problems with multiple constraints. Compared to previous approaches, our method effectively address challenges posed by cost functions that are non-strongly convex or even non-convex, while significantly reducing computation time. Subsequently, the local tracking controller layer is designed to ensure that the sailing states of the autonomous system can track the optimal signals obtained by the distributed optimization estimator. Additionally, we design a robust predefined-time tracking controller for autonomous systems capable of effectively handling unknown but bounded external disturbances. A control term is further incorporated to enable the autonomous system to achieve internal obstacle avoidance. Finally, we provide illustrative examples to demonstrate the validity and efficiency of the proposed predefined-time distributed optimization scheme.

In this work, a new tracking control algorithm based on integral reinforcement learning (IRL) is proposed for nonlinear Markov jump systems (MJSs) represented by interval type-2 fuzzy (IT2F) model. The adoption of IT2F approach to describe nonlinear objects can overcome the uncertainty problem of traditional Takagi–Sugeno (T-S) fuzzy model. The control input and external disturbance are considered as two opposing competitors, and the optimal control problem is transformed into a zero-sum game problem. Furthermore, a mode-free IRL algorithm is designed to solve the fuzzy-coupled algebraic Riccati equations without the system dynamics information. The stability and the convergence of new scheme are demonstrated through Lyapunov theory, and the desired tracking goal is achieved. Finally, the designed model-free IRL algorithm is applied to a typical mass-spring-damper mechanical system and the implementation results demonstrate the practicality and effectiveness of the proposed method.

For unknown continuous-time heterogeneous linear multiagent systems (MASs) under mixed denial-of-service (DoS) attacks, a novel reinforcement learning (RL) algorithm named hybrid iterative (HI) is proposed in this article to solve the secure output tracking problem based on a prescribed-time observer. Considering the scenario that MASs are subjected to mixed DoS attacks that can cause the connectivity maintained or broken of the network communication topology, a distributed resilient prescribed-time observer is designed to accurately estimate the leader’s state and output within a prescribed time. Then, the secure output tracking problem of heterogeneous MASs is converted into the optimal linear quadratic tracking (LQT) problem by introducing a discounted performance function, and inhomogeneous algebraic Riccati equations (AREs) are further derived to solve it. Meanwhile, an HI-based data-driven RL algorithm independent of the initial admissible control policy and the system dynamics knowledge is proposed to learn the optimal solution of inhomogeneous AREs. Compared with the traditional RL algorithms, that is, policy iteration (PI) and value iteration (VI), HI can not only remove the restrictions of the initial admissible policy in PI but also converge to the optimal solution faster than the VI. Finally, comparative simulation verifies the effectiveness of the theoretical results.

This paper investigates the cooperative fuzzy tracking control of nonlinear unmanned surface vehicles with input quantization and event-triggered mechanism. The proposed cooperative control scheme consists of two parts: (i) the distributed observer and (ii) the dynamic event-based fuzzy tracking controller. The distributed observer is designed to obtain the nonlinear leader’s trajectory information on a directed communication topology. Under this framework, uncertain nonlinearity within the vehicle model is approximated through fuzzy logic systems, and, according to the state of the distributed observer, the dynamic event-based adaptive fuzzy tracking control law is developed with an input switching quantizer. Furthermore, a prescribed performance method is introduced to ensure the transient performance of tracking errors and obtain zero-tracking errors ultimately, which is proved through Lyapunov stability theory. Finally, the effectiveness of the proposed control strategy is verified by simulation experiments.

With respect to the group consensus control for multi-agent systems (MASs) with disturbances and actuator nonidentical and unknown direction faults (NUDFs), this paper proposes a novel fault-tolerant control scheme by incorporating an event-based switching function and a saturation function. The event-based switching function is implemented to directly switch to the desired working mode, thereby reducing invalid switching after a reverse fault. Concurrently, the saturation function, acting at the software level, can not only prevent the instantaneous impact of excessive reverse input caused by a reverse fault, but also eliminate input peaking and state chattering after switching the working mode. It is worth mentioning that the proposed switching-based fault-tolerant control method can avoid the control chocks and excessive control gains as in the existing methods by using Nussbaum functions and thus improve the transient performance. Additionally, for disturbances, the designed conditional disturbance compensation mechanism ensures a smaller steady-state error, and less energy consumption by utilizing beneficial disturbances and compensating for harmful ones. Besides, the conditional compensation mechanism also reduces the saturation error. Finally, the effectiveness of the proposed control scheme are verified through a simulation example of unmanned aerial vehicles (UAVs).

In this work, the asynchronous dynamic output feedback control is investigated for discrete nonlinear networked semi-Markov jump models with cyber attacks, in which asynchronous phenomenon refers to the mode mismatch between the controller and the system. For the potential uncertainty of system parameters, the interval type-2 fuzzy method is adopted to characterize nonlinear semi-Markov jump models. In light of actual state information unavailable in complex environment, the dynamic output feedback technique is proposed. The main novelty is to construct an appropriate dynamic output feedback control scheme, fully consider the asynchronous phenomenon of the controller mode, and introduce auxiliary variables to solve the matrix dimensional problem, so that the IT2 fuzzy network semi-Markov jump models under the influence of cyber attacks have better dynamic performance. According to stochastic theory, semi-Markov kernel, interval type-2 fuzzy method, and dwell-time-dependent Lyapunov function, sufficient criteria are established to ensure that the networked system is$\vartheta $-error mean-square stable under random denial-of-service attacks. Furthermore, numerically checkable conditions are formulated to solve the asynchronous dynamic output feedback controller gain. Finally, a two-degree-freedom quarter-car suspension model is given to explain the superiorities of the proposed design approach. Note to Practitioners—As one of the research hotspots, networked control systems show outstanding advantages of low maintenance cost, easy installation, and high flexibility. However, cyber attacks often occur in networked control systems, posing a great threat to signal transmission. With the development of modern science and technology, semi-Markov jump models, owing to their excellent engineering background in modeling complex system, have a wide range of application prospects in solar receiver control, power electronics, chemical processes, and network communication. Note that some factors in the practical dynamical systems, such as parameter uncertainty and dwell information, cannot be completely obtained, and actual controller mode has switching delay with the system mode. In this paper, the asynchronous output feedback control is studied for discrete nonlinear networked semi-Markov jump models with cyber attacks. On the basis of interval type-2 fuzzy and dwell-time-dependent Lyapunov function, the$\vartheta $-error mean-square stability is realized under random denial-of-service attacks. This research provides a new approach to study output feedback control strategies for discrete networked systems.

This study addresses the problem of sliding mode control (SMC) for networked non-homogeneous hidden semi-Markov switching systems under semi-Markov kernel and cyber attacks, in which the limited dwell time information is related to the probability density function. Firstly, the model of networked non-homogeneous hidden semi-Markov switching systems under cyber attacks is constructed. Considering that it is difficult to obtain all the real modes in actual systems, a hidden semi-Markov chain is utilised to determine the hidden mode switching of the underlying system, which is more realistic than semi-Markov chain. Based on restricted dwell time probability density function, the common assumption is relaxed under completely known probability density function. The main innovation is to build a suitable SMC scheme under denial-of-service attacks to achieve quasi-sliding mode that eliminates the effect of uncertain parameters. By means of the Lyapunov function depending on the system mode and the elapsed time, stability criteria are considered for the corresponding model. Finally, a DC-DC buck converter circuit model is introduced to validate the practicality of the proposed strategy. Note to Practitioners—Note that the control community has witnessed a tremendous development in networked control systems with wide applications in many practical models, enabling remote control in a sensitive manner through communication networks. Although networked control systems offer obvious advantages, they always suffer from particular difficulties associated with network transmission, such as cyber attacks and packet losses. With the rapidly developing network and communication technologies, networked non-homogeneous hidden semi-Markov switching systems have attracted much attention due to strong capabilities in modeling dynamical systems with abrupt changes in structures or parameters. It is worth emphasising that it is difficult to obtain all real mode information of networked semi-Markov switching systems. This paper offers a novel approach for researchers to investigate the SMC for networked non-homogeneous hidden semi-Markov switching systems under semi-Markov kernel and cyber attacks.

In this paper, the sliding mode control (SMC) problem is investigated for continuous stirred tank reactor (CSTR) via dynamic self-triggered protocol subject to external disturbance. By introducing the switched stochastic system with semi-Markov parameter, the fluctuations in product concentration and the variations in reaction temperature within the CSTR are modeled. Without the sojourn-time constraint, novel strategy about asymptotic mean-square stability is adopted to design the SMC scheme based on the Lyapunov function and the stationary distribution of embedded Markov chain. Furthermore, to enhance the transmission efficiency and the dynamic performance, novel self-triggered protocol is developed in combination with SMC technology, predicting the subsequent triggered moment under the current sampling data and providing the lower bound of internal execution time. Under the dynamic self-triggered protocol, an appropriate SMC law is proposed to ensure the finite-time reachability of the specified sliding surface. Sufficient conditions are established for asymptotic mean-square stability of the associated switched stochastic systems. Finally, the proposed method is verified the simulation. Note to Practitioners—The continuous stirred tank reactor (CSTR) system is widely used in the industrial processes, in which the performance and control are critical to efficiency and quality. Under the key parameters of temperature and concentration, it is necessary to strictly control the temperature and monitor the concentration in real-time. Modern manufacturing and industrial processes have become increasingly complex, and single system can no longer accurately describe the dynamic characteristics. As a special hybrid system with continuous evolution, instantaneous change, and stochastic effect, switched stochastic semi-Markov system can describe the concentration fluctuations and reaction temperature changes in the CSTR, showing significant potential for applications. In recent years, networked control system has attracted attention due to their advantages in remote task execution. The self-triggered protocols conserve bandwidth and reduce dependence on external devices by predicting the next triggered time. In this work, switched stochastic system with semi-Markov parameter is adopted to investigate the self-triggered sliding mode control (SMC) problem for the CSTR. This research provides a novel perspective of SMC strategy based on switched stochastic semi-Markov system and self-triggered algorithm in the CSTR.

This paper deals with the synchronization for semi-Markovian jump neural networks (S-MJNNs) with DoS attacks and adaptive event-triggered protocol. Aiming at reducing some conservatism of sojourn-time-exponential distribution of Markovian process, the semi-Markovian process is adopted to describe the sudden changes in structures and parameters. As a class of networked systems, the securities of dynamical systems are vulnerable to the DoS attacks that are supposed to obey the frequency constraint. First, on the basis of fully considering the characteristics of aperiodic DoS attacks, a feedback controller is constructed to realize the synchronization among the master-slave systems, in which an adaptive event-triggered protocol is adopted to adjust the amount of triggered data and save network resources more effectively than traditional event-triggered protocol. Then, sufficient conditions to ensure the exponential synchronization and solvable controller gain of the underlying S-MJNNs are given by means of stochastic Lyapunov stability and integral inequality. Finally, the quadruple-tank process model is shown to verify the proposed method.Note to Practitioners—As one of the research hotspots of neural networks, synchronization plays a crucial role in pattern recognition, associative memory, and information science. With the development of computer science and network technology, the information transmission is carried out through the network in the synchronized control of neural networks, which is bound to be affected by the non-ideal network environment. Therefore, the research on synchronization control should not only consider the realization of control method, but also consider the influence of network delay, cyber attacks, and other non-ideal network environment on system performance from the perspective of communication network. In this paper, the synchronization is studied for S-MJNNs with DoS attacks and adaptive event-triggered protocol. On the basis of fully considering the characteristics of aperiodic DoS attacks, the synchronization among the master-slave systems is realized by means of multiple Lyapunov functions and integral inequality. This study provides a new method for the practitioners to study the synchronization control strategy of neural networks affected by the cyber attacks.

This paper investigates dynamic event-triggered resilient formation control for nonlinear multi-agent systems under multi-channel Denial-of-Service (DoS) attacks. Unlike existing resilient formation control strategies, which assume a unified attack model across all communication channels among followers under known system dynamics, which is not realistic. We propose a novel architecture for secure formation control to tackle the challenges posed by heterogeneous and uncertain dynamics, as well as distributed and asynchronous DoS attacks. Specifically, each communication channel, whether between the leader and followers, or among followers, may be independently and asynchronously attacked. In addition, a novel dynamic event-triggered mechanism that incorporates attack parameters is designed to mitigate the overuse of network bandwidth while avoiding the Zeno behavior. Notably, in comparison with conventional methods for heterogeneous cooperative systems, our approach eliminates the need for distributed observers to reconstruct the leader’s information, thus significantly reducing the transmission of additional variables and simplifying the system architecture. Finally, the effectiveness of our proposed algorithms is verified through a practical example involving a multi-robot system. Note to Practitioners—This paper investigates dynamic event-triggered resilient formation control for nonlinear multi-agent systems under multi-channel Denial-of-Service (DoS) attacks. The proposed distributed resilient control algorithms can be applied to multiple ground vehicles, air vehicles, and underwater vehicles. Unlike existing resilient formation control strategies, which assume a unified attack model across all communication channels among followers under known system dynamics, which is not realistic. We propose a novel architecture for secure formation control to tackle the challenges posed by heterogeneous and uncertain dynamics, as well as distributed and asynchronous DoS attacks. Specifically, each communication channel, whether between the leader and followers, or among followers, may be independently and asynchronously attacked. In addition, a novel dynamic event-triggered mechanism that incorporates attack parameters is designed to mitigate the overuse of network bandwidth while avoiding the Zeno behavior. Notably, in comparison with conventional methods for heterogeneous cooperative systems, our approach eliminates the need for distributed observers to reconstruct the leaders information, thus significantly reducing the transmission of additional variables and simplifying the system architecture. Finally, the effectiveness of our proposed algorithms is verified through a practical example involving a multi-robot system.

This paper investigates the distributed resilient output-tracking control problem for heterogeneous multi-agent systems (MASs) subject to multi-channel denial-of-service (DoS) attacks. First, we propose a simplified framework to address the distributed output tracking control for systems with uncertain dynamics. Then, we develop a distributed output feedback-driven resilient control protocol under asynchronous multi-channel DoS attacks, considering both inter-follower communication channels and leader-follower links. To enhance resilience against attacks, a set of channel-dependent decay rates is introduced and designed to ensure self-healing output tracking capabilities under active attacks. Unlike conventional distributed output control methods that heavily depend on distributed observers and assume the leader’s state is globally accessible or transmitted via distributed communication, our resilient control algorithm eliminates the need for distributed observers while reducing communication overhead through compact output information encoding. Finally, the effectiveness of the proposed algorithm is demonstrated through a practical example involving a multi-vehicle system.

In this paper, the optimal H∞control problem is investigated for a class of Markov jump linear systems, under event-triggering mechanism. To this end, a two-player zero-sum differential game-theoretic problem is formulated and a mode-dependent event-triggered version of Hamiltonian function is established and compared with its time-triggered counterpart. Based on this, the corresponding triggering event is given to ensure the stochastic stability of the considered system with a predefined H∞noise attenuation level index and the existence of a saddle point for the defined cost function is proved. The difference in the optimal cost between the time-triggered case and the event-triggered one is also shown. Further to derive an online solution without the full knowledge of the system model, aQ-learning framework is adopted to approximate the optimal cost and the optimal event-triggered control law combined with an actor-critic network. The error dynamics of the learning algorithm are analyzed and the convergence is proved. The stochastic stability of the resulting closed-loop system is also confirmed. Finally, a numerical example is given to show the effectiveness of the proposed results.

This paper investigates the event-triggered output path-tracking control of networked heterogeneous multi-vehicle (agent) systems with unknown model dynamics. Different from most existing distributed observer methods to estimate the leader vehicle’s state matrix and state, these state-based observer approaches raise the disadvantages of high dimensionality and high frequency of data exchange of state. To address this, in this paper, we propose a novel adaptive distributed output observer (ADOO) that estimates the coefficients of the minimal polynomial instead of requiring knowledge of all the entries of the leader vehicles system matrix. Moreover, our proposed ADOO is model-free without relying on the leader’s accurate system, unlike the model-based way in existing works. Meanwhile, an asynchronous dynamic event-triggered control strategy is developed to reduce the communication load among neighboring vehicles. Then, a decentralized path-tracking controller is learned via a model-free matrix updating learning technique to achieve optimal path-tracking control without requiring an initial stabilizing control policy. By rigorous mathematical analysis shows that our proposed algorithms not only can greatly reduce the dimension of existing observer methods and the frequency of information exchange among neighboring vehicles, but also exclude the Zeno phenomenon. Finally, the numerical simulation is used to validate the efficiency of the theoretical algorithms under investigation. Note to Practitioners—This paper investigates the learning-based distributed optimal path-tracking control for heterogeneous vehicle systems with event-triggered communications. The proposed distributed control algorithms can be applied to multiple ground vehicles, air vehicles, and underwater vehicles. Unlike existing results on distributed state observer methods, the obtained results rely on accurate system dynamics and ignore the transient performance, which makes the designed controller far from optimal in potential applications. Moreover, the optimal learning algorithm strictly relies on the initial stabilizing control policy related to accurate system dynamics, and it may be helpless when the system model is completely unknown. To overcome those issues, we developed a model-free matrix updating learning technique to study distributed output path tracking control by collecting system data instead of using accurate system dynamics. Our algorithm not only overcomes the initial stabilizing assumption but also ensures the algorithm convergence in a model-free fashion. Besides, our proposed distributed observer can greatly lower the dimension and the frequency of information exchange of existing observers. Potential applications of the proposed control algorithms include cooperative formation control of heterogeneous unmanned systems.

This study investigates the problem of adaptive neural network asynchronous control for switching cyber-physical systems under unknown dead zones. A generalized switching rule, instead of a Markov/semi-Markov process, is utilized to scrutinize the switching behavior of subsystems. This approach characterizes the dynamic nature of sojourn probabilities using single-mode-based sojourn time, aiming to decrease computational load while meeting the demands of real-world scenarios. Considering the intricacies of network environments, the unknown dead zone inputs are considered, which can be effectively implemented via the adaptive neural network-based control law. To counteract the adverse effects of unforeseen information, a saturation-based observer is developed, in which the saturation level is dynamically adjusted with the hope of providing greater flexibility. Utilizing a Lyapunov function that correlates with the detected mode and the system mode, sufficient criteria are established to ensure that the closed-loop system remains bounded in probability. Eventually, the practicality and effectiveness of the proposed control methodology are verified through two simulated examples.

The primary focus of this article centers around the application of sliding mode control (SMC) to semi-Markov jumping systems, incorporating a dynamic event-triggered protocol (ETP) and singular perturbation. The underlying semi-Markov singularly perturbed systems (SMSPSs) exhibit mode switching behavior governed by a semi-Markov process, wherein the variation of this process is regulated by a deterministic switching signal. To simultaneously reduce the triggering rate and uphold the system performance, a novel parameter-based dynamic ETP is established. This protocol incorporates weight estimation of a radial basis function neural network (RBFNN) and introduces two internal dynamic variables. Following the Lyapunov's theory, sufficient criteria are established for ensuring the mean-square exponential stability of the resulting system. Additionally, an SMC scheme based on the convergence factor is designed to fulfill reachability conditions. Finally, two examples are carried out to validate the solvability and applicability of the attained control methodology.

This article addresses the cooperative tracking control problem for heterogeneous multiple marine vehicles with a nonautonomous leader. A fully distributed smooth observer is proposed to estimate the trajectory of the leader, mitigating the influence of its control input. Based on the observer, three decentralized adaptive fuzzy event-based controllers are designed with distinct triggering strategies, i.e., fixed, relative, and switching threshold triggering strategies, which utilize fuzzy-logic systems and event-triggering mechanisms to address the challenge of model uncertainties and communication constraints of marine vehicles. The proposed methods ensure the zero-error tracking without Zeno behavior, as demonstrated through Lyapunov analysis. Numerical simulations validate the effectiveness of the proposed approaches.

The high-order fully actuated systems (HOFASs) approach can only completely eliminate known nonlinearities. However, the practical systems often encounter unknown nonlinearities, including disturbances and faults. Therefore, this article studies a class of nonlinear HOFAS with component faults and disturbances. By applying the HOFAS theory, a novel integrated sliding mode fault-tolerant control strategy is proposed to ensure the stability of the closed-loop system. The state feedback and output feedback controllers are designed, respectively. For output feedback control, an extended state observer is designed to estimate system states. Once the HOFAS model is established, the designed controller and extended state observer can be directly implemented, facilitating the analysis and design of the control system. And the stability analysis does not depend on the complexity of the nonlinear functions. Finally, a numerical simulation example shows the effectiveness of the proposed method.

In this article, we study the optimal output synchronization problem (OOSP) for uncertain networked Euler-Lagrange (EL) systems. Specifically, the system outputs are expected to be synchronized at the solution of an uncertain distributed time-varying quadratic optimization problem, where each local time-varying cost function includes uncertain parameters. From a centralized perspective, we first develop a controller with adaptive control gains to guide the output of a double-integrator system toward the time-varying optimal solution. By employing the modified average estimators, we extend the centralized design to a distributed implementation to address the OOSP for uncertain EL systems. Using matrix trace properties and composite Lyapunov analysis, we prove that the system outputs can asymptotically converge to the desired time-varying optimal solution. Two examples are used to verify the proposed designs.

State estimation of cyber-physical power systems (CPPSs) is of great significance for power system optimization, control, and security analysis. Additionally, fractional differential calculus is based on differentiation and integration of arbitrary fractional order, which can more accurately describe the physical phenomenon model than the traditional integer calculus. Thus, this article proposes a novel fractional-order extended Kalman filter (FOEKF) based on the evolutionary algorithm and deep ensemble learning techniques for the state estimation problem of CPPSs from the fractional-order theory perspective. First, the power system is modeled as a fractional version to describe the physical phenomenon better according to the fractional differential calculus theory. Then, considering the difficulties in determining fractional orders in the fractional-order power system, a deep ensemble learning-based approach is used to design the fitness function and a genetic algorithm is developed to determine these parameters by optimizing the designed objective function. Furthermore, to solve the difficulties in estimating for fractional-order power system by integral extended Kalman filter (EKF), the evolutionary FOEKF (EFOEKF) is presented as the estimator for the designed fractional-order power system. Finally, to improve the performance of EFOEKF under bad datum scenarios caused by cyber-attacks or sudden loads, an enhanced EFOEKF method is developed by using an adapted exponential weighting function. The numerical simulation results show that the proposed EFOEKF is better than EKF and FOEKF on four different IEEE bus systems in terms of the mean absolute error.

In this study, an improved jump model is proposed for the Roesser-type 2-D Markov jump systems (MJSs). We use two independent Markov chains that propagate along the horizontal and vertical directions, respectively, to characterize the switching of system dynamics in those two directions. Compared with the conventional jump model, which uses only one Markov chain to characterize the switching of system dynamics in both directions, the newly proposed 2-D jump model shows better modeling capabilities for real-world applications with abrupt changes while inherently avoiding the mode ambiguity phenomenon. Based on the proposed jump model, we then propose a dual-mode-dependent state feedback control law to stabilize the concerned 2-D MJS. A sufficient criterion, whose feasibility is enhanced via a dual-mode-dependent Lyapunov functional technique, is obtained to ensure the asymptotic mean square stability and $H_{\infty }$ disturbance attenuation level of the resulting closed-loop system. Subsequently, resorting to a novel nonconservative separation principle, two equivalent conditions with one of them in the form of linear matrix inequalities (LMIs) are developed. Finally, a convex optimization algorithm which is formulated by the obtained LMIs is proposed to design the control law. An example of the Darboux equation with Markov switching parameters is presented to validate the effectiveness of the obtained results.

In this article, a network attack, named shifting attack, is considered for hidden Markov Boolean control networks (HMBCNs). Using semi-tensor product of matrices, the considered network and the network attack are presented in algebraic form. State feedback control (SFC) is then applied to stabilize the considered network to a desired state. A necessary and sufficient condition based on the probability matrix is presented for the stochastic stabilization of the HMBCN, based on which, the design of the SFC is given. Then shifting attack is further studied for HMBCNs. The control strategy will be shifted to another when the HMBCN is under attack. Shifting attack is also modeled in a hidden Markov process, under which, a necessary and sufficient condition for the security of the attacked HMBCN is also presented. Propositions are obtained for the security and insecurity of the attacked HMBCN. Using the change of the probability measurement approach, estimations of the states expectation and the attacked signals expectation for the attacked HMBCN are solved. At last, examples show the effectiveness of the obtained results.

The passivity-based asynchronous control is tackled for 2-D Roesser Markovian jump systems (MJSs) and stabilization is guaranteed when 2-D MJSs are susceptible to Denial-of-Service (DoS) attacks. A novel jump model is proposed in this article, where the switching law of subsystems is regulated by the sum of the horizontal and vertical coordinates' values. This differs from the conventional jump model, which presumes that the transition probabilities are identical in both directions. The proposed jump model can avoid the mode ambiguity problem. Given the openness and sharing nature of communication networks, they are susceptible to malicious cyber-attacks that impair system performance. The concept of global time is introduced to help characterize the jump law and construct DoS attack model. Besides, a hidden Markov model (HMM) is utilized to manage the inevitable mismatched mode problem induced by any delay or data dropouts. With the above considerations, several conditions are established for ensuring passivity performance of 2-D MJSs and stabilization when facing DoS attacks. Several equivalent solvable conditions are derived via decoupling strategy and matrix inequality technique. Finally, two simulation examples are provided to demonstrate the validity of the established theoretical results.

This paper addresses the data-driven safe prescribed performance lane-changing control problem of vehicle platoons with unknown dynamic models. As the safe guarantee for the lane changing, the collision avoidance condition is built among the vehicle platoon and surrounding vehicles firstly. Then, a desired collision-free driving trajectory is designed for the leader vehicle, which guides the vehicle platoon to complete the lane changing under the time-varying output constraint. To remove the dependence on the system output at the next time in the controller design of the existing work, a novel dynamic linearization data model is developed to relate the unconstrained prescribed performance tracking error to the system input. Based on these developments, a creative data-driven prescribed performance lane-changing control scheme is proposed only using the input-output data for the first time. Through the rigorous proof, the controlled vehicle platoon can safely complete the lane-changing maneuver with the pre-given accuracy within predefined finite time step. Finally, a vehicle platoon example with the comparative analysis is provided to validate the effectiveness of the proposed lane-changing control algorithm.

This article investigates the distributed control problem for nonlinear multiagent systems (MASs) with unknown system models. A novel distributed model-free adaptive learning algorithm is developed to learn a controller from the online system data. Notably, a significant advancement over conventional methods is that the proposed algorithm requires only local interaction data from neighboring agents, eliminating dependencies on both a priori system structural knowledge and global topology information. Comprehensive simulations validate the theoretical results and demonstrate the superior efficacy of the devised algorithm.

This article studies distributed continuous-time optimization for time-varying quadratic cost functions with uncertain parameters. We first propose a centralized adaptive optimization algorithm using partial information of the cost function. It can be seen that even if there are uncertain parameters in the cost function, exact optimization can still be achieved. To solve this problem in a distributed manner when different local cost functions have identical Hessians, we propose a novel distributed algorithm that cascades the fixed-time average estimator and the distributed optimizer. We remove the requirement for the upper bounds of certain complex functions by integrating state-based gains in the proposed design. We further extend this result to address the distributed optimization where the time-varying cost functions have nonidentical Hessians. We prove the convergence of all the proposed algorithms in the global sense. Numerical examples verify the proposed algorithms.

In this study, a novel self-learning control is proposed for nonlinear quarter-car suspension system (QCSS) based on Markov jump model with completely unknown dynamics to solve the optimal control issue. The interval type-2 (IT2) fuzzy method is used to overcome the uncertainty problem and the zero-sum game approach is adopted to transform the optimal control issue, so as to achieve Nash-equilibrium. Based on the framework of reinforcement learning (RL), an off-line policy iterative algorithm is proposed to solve the fuzzy random coupled algebraic Riccati equation (ARE). Due to the complexity and variability of the actual system, it is difficult to obtain the complete dynamic information. A self-learning algorithm is designed based on the off-line algorithm, which does not require any dynamic information of the system, relying on the system state and control input for iteration. Furthermore, the Lyapunov stability theory is adopted to ensure that the system is asymptotically stable withH∞performance index. Finally, a simulation example is given to explain the effectiveness of the control method.

In this article, fault detectability of Boolean control networks (BCNs) is analyzed via a labeled graph approach. First, matrix-based representations of nonfault BCNs and fault BCNs are constructed by using the semi-tensor product (STP) of matrices. Based on these matrix representations, labeled graphs are further developed for nonfault BCNs and fault BCNs, respectively. Then, the passive fault detectability (PFD) is solved by labeled graph of the nonfault BCN. Meanwhile, based on the labeled graphs of nonfault BCNs and fault BCNs, the active fault detectability (AFD) is further studied. By leveraging labeled graphs, two sufficient criteria for strong AFD and AFD can be derived without the need for iterative matrix calculations, thereby significantly reducing computational complexity. Furthermore, the corresponding necessary and sufficient criteria are further derived when these two sufficient criteria are invalid. Finally, a biological system for the lac operon in$Escherichia~coli$is elaborated to verify the effectiveness of obtained results.

For the input-delayed stochastic systems with the states and input quantization, the adaptive stabilization problem is investigated in this article. The whole control scheme design process can be divided into three steps. First, the traditional adaptive neural control scheme is developed for the controlled system. Next, the effective control scheme is proposed for the system with the quantized states. Finally, the adaptive neural control method is developed for the considered system with the states and input quantization. The radial basis function neural network (RBFNN) is applied to approximate the unknown terms online, and the Pade approximation method is introduced to deal with the input-delayed problems. The adaptive neural fault control strategy is presented to address sensor faults and the discontinuity due to the quantized states. Under the constructed controllers, all the closed-loop signals remain semi-globally uniformly ultimately bounded (SGUUB) in mean square. The effectiveness and superiority of the presented control schemes are verified by some simulation results.

This article proposes online data-based reinforcement learning (RL) algorithm for adaptive output consensus control of heterogeneous multiagent systems (MASs) with unknown dynamics. First, we employ the adaptive control technique to design a distributed observer, which provides an estimation of the leader for partial agents, thereby eliminating the need for the global information. Then, we propose a novel data-based adaptive dynamic programming (ADP) approach, associated with a double-integrator operator, to develop an online data-driven learning algorithm for learning the optimal control policy. However, existing optimal control strategy learning algorithms rely on the persistent excitation conditions (PECs), the full-rank condition, and the offline storage of historical data. To address these issues, our proposed method learns the optimal control policy online by solving a data-driven linear regression equations (LREs) based on an online-verifiable interval excitation (IE) condition, instead of relying on PEC. In addition, the uniqueness of the LRE solution is established by verifying the invertibility of a matrix, instead of satisfying the full-rank condition related to PEC and historical data storage as required in existing algorithms. It is demonstrated that our proposed learning algorithm not only guarantees optimal tracking with unknown dynamics but also relaxes some of the strict conditions of existing learning algorithms. Finally, a numerical example is provided to validate the effectiveness and performance of the proposed algorithms.

This work studies the finite-time self-triggered control of networked nonlinear unmanned marine vehicle (UMV) systems with hybrid attacks. A Takagi–Sugeno (T–S) fuzzy model is constructed to characterize the nonlinear UMV systems. To save limited communication and computing resources, an intelligent self-triggered mechanism is proposed, in which the threshold of self-triggered condition is adjusted intelligently by theQ-learning algorithm. Only the current states information and the last samples are adopted to calculate the interexecution interval for the next triggered instant, and then the controller signal is updated. In light of denial-of-service attacks and deception attacks under networked environment, two Bernoulli random variables are applied to describe the random occurrence of hybrid attacks. By using the Lyapunov function, sufficient conditions for finite-time boundedness of the closed-loop UMV systems are obtained. In addition, a collaborative design method for triggered parameter and controller gain is proposed. Finally, the benchmark UMV systems are simulated to demonstrate the effectiveness of the proposed strategy.

The secure consensus problem is addressed for multiagent systems (MASs) suffering from saturated control input and denial-of-service (DoS) attacks. The communication networks’ open setting and sharing nature give rise to security issues and impact the performance of MASs. Malicious DoS attacks attempt to disrupt the information exchange and undermine consensus by compromising the availability of transmitted data. Moreover, the control input can be saturated as a result of physical device limitations or safety concerns. To tackle these challenges, a state-prediction-based dynamic event-triggered mechanism (DETM) control protocol is designed to guarantee the secure consensus of MASs while reducing redundant communication, avoiding continuous monitoring of adjacent states, and ensuring effective utilization of limited bandwidth resources. Zeno behavior is eliminated by confirming the existence of a positive lower bound on interevent intervals. Sufficient conditions are established for the co-design of the DETM and controller to accomplish the desired goal. Finally, a simulation is conducted to substantiate the effectiveness and validity of the proposed control protocol.

Traditional process monitoring methods often rely on data with uniform sampling rates, which may lead to the loss of valuable information across both time and space dimensions. Moreover, multirate data exhibits strong autocorrelation and cross-correlation among various sampling rates. Effectively capturing these characteristics is crucial for accurately monitoring process variations. In this article, a total structure multirate autoregressive dynamic latent variable (Ts-MARDLV) model is proposed, which establishes global dynamic latent variables for all measurements and local static latent variables for each sampling rate, effectively analyzing the autocorrelation and cross-correlation of samples. For multirate process monitoring, the Ts-MARDLV model-based fault detection schemes are developed. Three diverse fault detection statistical metrics are constructed to monitor faults in different latent spaces. The proposed method is validated on multiphase flow datasets and a real papermaking wastewater process, demonstrating its superior effectiveness compared to single or multirate methods.

This paper is devoted to the sliding mode control issue of switching singularly perturbed systems in the presence of dynamic-memory event-triggered protocol. A novel nonhomogeneous sojourn probability is introduced with the hope of better characterizing the switching behavior of the corresponding system mode, in which the variation of sojourn probabilities is adjusted by a deterministic switching signal. Additionally, aiming at relieving the communication burden and improving improve the system performance, a novel dynamic event-triggered protocol with the merits of utilizing a series of historically launched packets and the singularly perturbed parameter is constructed. Subsequently, a singularly-perturbed-parameter-based sliding mode controller is designed for ensuring the mean-square exponential stability of the switching singularly perturbed systems and the reachability of the specified sliding surface. Finally, two simulation examples are provided to verify the efficiency of the theoretical findings.Note to Practitioners—Sliding mode control has gained broad attention for its cost-saving and reliability. In some practical networked control systems, due to the inner property, they will bring a great burden of computation and data transmission. In this paper, we give a novel dynamic event-triggered protocol with the merits of utilizing a series of historically launched packets and the singularly perturbed parameter. A novel nonhomogeneous sojourn probability is introduced with the hope of better characterizing the switching behavior of the corresponding system mode, in which the variation of sojourn probabilities is adjusted by a deterministic switching signal. A singularly-perturbed-parameter-based sliding mode controller is provided to make the networked control systems keep stable. The results provide a reference for the mean-square exponential stability of the switching singularly perturbed systems and the reachability of the specified sliding surface under sliding mode control.

The observer-based stabilization of network-based interval type-2 (IT2) semi-Markov jump models is investigated, where a new control strategy based on redundant channels is proposed to overcome the adverse effects of packet loss. The IT2 fuzzy method is adopted to describe the nonlinear objects, which overcomes the uncertainty problem of the traditional T-S fuzzy model. An observer-based control strategy is adopted due to the difficulty of obtaining complete state information under a complicated network environment. The main novelty is that redundant channels are adopted to construct a suitable observer-based feedback control scheme, and the auxiliary variable is introduced to solve the matrix dimension problem to achieve better dynamic performance of IT2 fuzzy networked semi-Markov jump models, improve the stability and anti-interference ability of the system, and overcome the difficulty caused by potential packet loss. Based on the semi-Markov kernel, IT2 fuzzy, and the Lyapunov functions dependent on the elapsed time, sufficient criteria are constructed for the considered system to ensure the$\sigma$-error mean-square stability. Furthermore, the expected solvability is constructed for an observer-based feedback controller under a linear matrix inequality framework. Ultimately, the tunnel circuit model verifies the proposed control method.

The issue of adaptive protocol-based control is studied for nonhomogeneous stochastic semi-Markov jump systems with denial-of-service (DoS) attacks. As a result of cyber attacks occurring during networked information transmission, the security controller design for the underlying nonhomogeneous semi-Markov jump systems becomes significantly more complicated. Different from the existing self-triggered strategy, an adaptive self-triggered strategy is proposed to reduce the updating frequency of the controller by adjusting the on-line threshold. The main novelty lies in constructing an appropriate adaptive self-triggered strategy to realize better dynamic performance, overcoming the difficulty caused by DoS attacks. By using the algebraic relationship among transition probability, probability distribution function, and probability density function, the time-varying transition rate is expressed based on the semi-Markov process. According to adaptive self-triggered strategy and DoS attacks, stochastic stability conditions are established. Furthermore, the desired adaptive self-triggered controller gains are derived to realize the stochastic stability for the corresponding system. Finally, the boost converter circuit model is carried out to ensure the feasibility of the proposed theoretical results.

The neural network-based adaptive backstepping method is an effective tool to solve the cooperative tracking problem for nonlinear multiagent systems (MASs). However, this method cannot be directly extended to the case without continuous communication. It is because the discontinuous communication results in discontinuous signals in this case, the standard backstepping method is inapplicable. To solve this problem, a hierarchical design scheme that involves distributed cooperative estimators and neural network-based decentralized tracking controllers is proposed. By introducing a dynamic event-triggered mechanism, cooperative intermediate parameter estimators are first designed to estimate the unknown parameters of the leader. By using the interpolation polynomial method, these estimators are extended to smooth estimators with high-order derivatives to guarantee that the backstepping method is applicable. Based on the state of the smooth estimators, a backstepping-based decentralized neural network tracking controller is designed. It is shown that the tracking errors are asymptotically convergent and all the signals in the closed-loop systems are bounded. Compared with the existing cooperative tracking results for nonlinear MASs with event-triggered communication, a more general class of MASs is considered in this article and a better performance in terms of asymptotic tracking is achieved. Finally, a simulation example is given to show the effectiveness of our developed method.

The robust LQ optimal regulator problem for discrete-time uncertain singular Markov jump systems (SMJSs) is solved by introducing a new quadratic cost function established by the penalty function method, which combines the penalty function and the weighting matrices. First, the indefinite robust optimal regulator problem for uncertain SMJSs is transformed into the robust optimal regulator problem with positive definite weighting matrices for uncertain Markov jump systems (MJSs). The transformed robust LQ problem is settled by the robust least-squares method, and the condition of the existence and analytic form of the robust optimal regulator are proposed. On the infinite horizon, the optimal state feedback is obtained, which can guarantee the regularity, causality, and stochastic stability of the corresponding optimal closed-loop system and eliminate the uncertain parameters of the closed-loop system. A numerical example and a practical example of DC motor are used to verify the validity of the conclusions.

In this article, a synchronization control method is studied for coupled neural networks (CNNs) with constant time delay using sampled-data information. A distributed control protocol relying on the sampled-data information of neighboring nodes is proposed. Lyapunov functional is constructed to analyze the synchronization of CNNs with constant time delay. Using Park's integral inequality and improved free-weight matrix integral inequality, sufficient conditions are provided for CNNs to achieve synchronization with less conservatism. In addition, the maximum sampling interval is determined by transforming the sufficient conditions into an optimization problem, and an aperiodic sampling control technique is implemented to reduce the communication energy load. Finally, numerical simulations are provided to demonstrate that the proposed method is capable of achieving synchronization.

Approximation models play a crucial role in model-based methods, as they enhance both accuracy and computational efficiency. This article studies distributed and asynchronous discretized models to approach continuous-time nonlinear systems. The considered continuous-time system consists of some distributed but physically coupled nonlinear subsystems that exchange information. We propose two Lebesgue approximation models (LAMs): 1) the unconditionally triggered LAM (CT-LAM) and 2) the CT-LAM. In both approaches, a specific LAM approximates an individual subsystem. The iteration of each LAM is triggered by either itself or its neighbors. The collection of different LAMs executing asynchronously together form the approximation of the overall distributed continuous-time system. The aperiodic nature of LAMs allows for a reduction in the number of iterations in the approximation process, particularly when the system has slow dynamics. The difference between the unconditionally and CT-LAMs is that the latter checks an "importance" condition, further reducing the computational effort in individual LAMs. Furthermore, the proposed LAMs are analyzed by constructing a distributed event-triggered system which is proved to have the same state trajectories as the LAMs with linear interpolation. Through this specific event-triggered system, we derive conditions on the quantization sizes in LAMs to ensure asymptotic stability of the LAMs, boundedness of the state errors, and prevention of Zeno behavior. Finally, simulations are carried out on a quarter-car suspension system to show the advantage and efficiency of the proposed approaches.

This article is devoted to data-driven event-triggered adaptive dynamic programming (ADP) control for nonlinear systems under input saturation. A global optimal data-driven control law is established by the ADP method with a modified index. Compared with the existing constant penalty factor, a dynamic version is constructed to accelerate error convergence. A new triggering mechanism covering existing results as special cases is set up to reduce redundant triggering events caused by emergent factors. The uniformly ultimate boundedness of error system is established by the Lyapunov method. The validity of the presented scheme is verified by two examples.

This article intends to study the asynchronous control problem for 2-D Markov jump systems (MJSs) with nonideal transition probabilities (TPs) under the Roesser model. Two practical considerations motivate the current work. First, considering that the system mode cannot always be observed accurately, a hidden Markov model (HMM) is adopted to describe the relationship between the mismatched modes. Second, considering that the TPs information related to the Markov process and the observation process is difficult to obtain, the nonideal TPs (unknown or uncertain) are simultaneously considered on the two processes. Under the considerations, several new sufficient conditions are developed for concerned closed-loop 2-D MJSs with nonideal TPs, by which the asymptotic mean square stability is ensured with an${\mathcal {H}}_{\infty }$performance index. A nonconservative separation strategy is utilized to decouple the system mode TPs and the observation TPs to facilitate the analysis of nonideal TPs. An unified LMI-based condition is finally developed for the concerned closed-loop 2-D MJSs with/without nonideal TPs, showing more satisfactory conservatism than that in the literature. In the end, we present two examples to validate the superiority of the proposed design method.

Asymptotic observability of distributed Boolean networks (DBNs) is studied in this article. Via a parallel extension method, asymptotic observability of the original system is converted to reachability at a fixed point of the extended system. Based on the structure matrix of the extended system, a necessary and sufficient condition is presented for asymptotic observability. Further, for unobservable systems, mode-dependent pinning control is first introduced and applied to achieve asymptotic observability, including the selections of pinning nodes, the design of output feedback controls, and the adding approaches. Then, a set of matrices is defined for the construction of the desired structure matrix. Based on it, a necessary condition is given to guarantee the solvability of the corresponding output feedback controls and the adding approaches. Finally, a numerical example is presented to show the effectiveness of the obtained results.

This article investigates a nonlinear disturbance observer (NDO)-based fault-tolerant sliding-mode control (SMC) for 2-D plane vehicular platoon systems subjected to actuator faults with unknown time-varying fault direction (UTVFD), asymmetric nonlinear actuator saturation (ANAS), nonlinear unmodeled dynamics, and unknown external disturbance. The Nussbaum-type function approach is adopted to solve the problem of actuator faults with UTVFD. The designed NDO not only can estimate the lumped disturbance accurately but also can reduce the control peaking and chattering phenomena caused by the Nussbaum-type function. Then, an adaptive saturation compensator is designed to compensate for the influence of actuator saturation on the system. In addition, by combining SMC technology with the prescribed tracking performance (PTP) approach, a distributed fault-tolerant control scheme is developed to not only ensure collision avoidance and communication connectivity but also realize a variety of driving scenarios, such as multilane vehicle merging and vehicular platoon lane changing. Finally, simulation results are presented to show the proposed scheme's effectiveness and advantages.

This article proposes the two-layer asynchronous control scheme for a class of networked nonlinear jump systems. For the constructed system in a network environment, the data transmission may suffer from many restrictions, such as incomplete acceptable mode information and transition information, nonlinearity of system and inadequate bandwidth resources, etc. Then, the two-layer asynchronous controller is developed to stabilize the plant constructed by Takagi-Sugeno (T-S) fuzzy method and semi-Markov theory (SMT). Herein, the hidden semi-Markov process with time-varying emission probability is introduced to establish the relation between the system modes and the controller modes, in which the interval segmentation method is presented to deal with this time-varying probability. Compared with some published results, this method can make full use of the transition rate information, which may lead to the reduction of conservatism in the proposed asynchronous control design. At the same time, the limited bandwidth problem in the communication channel is addressed by introducing the bilateral quantization strategy, and the new sufficient conditions are derived on the stochastic stability of the nonlinear jump system with/without incomplete transition and sojourn-time information. Finally, the numerical simulation examples about DC motor illustrate the effectiveness and the feasibility of the proposed approach.

The event-based$H_{\infty }$control problem is investigated for a class of nonhomogeneous Markov jump systems (MJSs) with partially unknown transition probabilities (TPs). The MJS is characterized by a piecewise nonhomogeneous Markovian chain, where the switching of the system TP matrix is governed by a higher-level chain. A hidden Markov model (HMM) is employed to observe the system mode, which cannot always be correctly detected in practice. Under this framework, the partially unknown TPs existing in both higher-level TPs (HTPs) and conditional TPs (CTPs) are taken into account for practical consideration. Additionally, an observed-mode-dependent event-triggered mechanism (ETM) is employed to design an asynchronous controller, which is expected to alleviate the burden of the communication network. Evidently, the considered scenario is fairly general and covers some special cases. With the above consideration, sufficient conditions are established to guarantee stochastic stability of the resulting closed-loop system with a prescribed$H_{\infty }$performance. Finally, two examples are presented to demonstrate the effectiveness and applicability of the proposed method.

This article addresses the resilient consensus problem of multiagent systems subject to cyber attacks on communication links, where the attacks on different links may collude to maintain undetectable. For the case with noncollusive attacks on links, a distributed fixed-time observer is designed so that the attack on each link can be detected by the two associated agents. A necessary and sufficient condition is derived to ensure the isolation of attacked links and no mistaken isolation of normal ones. For the case with collusive attacks on links, a novel attack isolation algorithm is proposed by constructing extra observers on the basis of the previous designed distributed fixed-time observer via sequentially removing the information associated with one of the links. Based on the isolation of the attacked links, a control algorithm is designed, and a necessary and sufficient condition is provided to achieve resilient consensus. Numerical examples corroborate the effectiveness of the proposed strategies.

The article investigates the decentralized periodic dynamic event-based load frequency control problem for a class of multiarea nonlinear power systems with uncertain parameters. For overcoming the limitations on the knowledge of studied power systems, the interval type-2 (IT2) fuzzy model is synthesized by using local linear models relevant to some operation points. Under the IT2 fuzzy framework, the decentralized periodic dynamic event-based fuzzy control law is proposed to reduce the bandwidth burden of communication networks. Based on the Lyapunov stability theory, a sufficient condition is presented such that closed-loop systems are exponentially stable with a given$H_{\infty }$performance. The existence condition of the controller gains and the triggering scheme's parameters is expressed in terms of matrix inequalities. The obtained results are extended to two situations, i.e., the decentralized periodic static event-based fuzzy control and the decentralized periodic sampling fuzzy control. Compared with the latter two control approaches, the developed decentralized periodic dynamic triggering strategy can provide the lowest communication frequency. Finally, the validity and superiority of the developed method are demonstrated by simulation results.

This article considers the dynamic output feedback model predictive control (DOFMPC) for the constrained Takagi-Sugeno (T-S) model with bounded disturbance. Unlike the existing approach where the robust positively invariant set is characterized by a single ellipsoid, we characterize it by the intersection of multiple ellipsoids, each corresponds to a vertex sub-model of the T-S model realization. The previous single ellipsoid is then an inner approximation of the intersection of multiple ellipsoids in this article. Therefore, the performance can be improved. We also generalize the multi-ellipsoid approach to the previous multi-step approach and formulate the so-called multi-step multi-ellipsoid approach in this article, which can further enlarge the feasibility region and enhance the performance. The recursive feasibility and the convergence of the approach are guaranteed. The proposed approaches are compared through a numerical problem to show their effectiveness.

This article studies the tracking control problem of uncertain Euler–Lagrange systems. Despite receiving widespread attention in recent years, the problem remains unresolved to a large content when considering response quality, optimality, robustness, and conservatism. The main challenge lies in how to integrate performance constraints into adaptive dynamic programming (ADP) algorithm and achieve a balance between robustness and conservatism within this framework. To that end, this study proposes a new performance constraint-handling sliding mode manifold, a new prescribed performance function, and a new ADP-oriented observer disturbance observer. The above theoretical findings, together with the fuzzy logic system-based ADP algorithm, realize the convergence of tracking errors to a prespecified residual set within a finite-time setting in an optimal manner, enhance robustness, and reduce conservatism. The proposed controller facilitates the practical application of tracking control for Euler–Lagrange systems. Simulations on an autonomous underwater vehicle demonstrate the effectiveness and benefits of the proposed method.

This paper investigates the asynchronous eventtriggered control problem for nonlinear Markov jump systems with complex transition probabilities. The nonlinear dynamics are captured by a polynomial fuzzy model, with fewer fuzzy rules and stronger approximation capacity compared with T-S fuzzy counterpart. In view of engineering applications, system mode information is not fully identified, including the mode state and the mode transitions. Here a hidden Markov model is utilized to serve as a detector of the original system mode. The transition probability of the system and the associated conditional transition probability for the joint process are both assumed to be general, which contains unknown or inaccurately known values. An event-based mode-dependent fuzzy controller is then constructed such that the considered nonlinear Markov jump systems can be stabilized, for the purpose of reducing data transmission and power consumption. With the aid of a new decoupling strategy, feasible conditions are derived and included in sum of square based theorems. Further the concept of imperfectly premise matching scheme is introduced to facilitate the membership-function-dependent stability analysis, so as to inject richer information of the membership function and obtain less conservative results. The flexibility of controller design is thus endowed. Examples are given to show the effectiveness of our obtained results.

The focus of this article is to address the asynchronous control issue for a class of nonlinear Markov jump systems with parameter uncertainties. They are represented by the interval type-2 polynomial fuzzy model. A hidden Markov model is utilized to describe the asynchronous behavior between the system mode and the controller mode in a quantitative manner. This is achieved by using a joint random process, which is presented in a concise format and encompasses both spontaneous and simultaneous jumps. Further to facilitate the design flexibility and low implementation burden, the structure of the asynchronous polynomial fuzzy controller is formed based on the imperfect premise matching scheme. Facing both mismatched modes and mismatched premise variables, a joint Markov chain-based membership-function-dependent stability criteria is established by utilizing the Lyapunov–Krasovskii theory and the$\mathcal {H}_\infty$performance analysis is then conducted using a sum-of-square approach. The polynomial feedback gain parameters can be readily solved by a sum of squares optimization toolbox SOSTOOLS. Finally, a numerical example is used to validate the effectiveness of our obtained results and the application potential is verified by a single-link robot arm model.

Load frequency control (LFC) is widely employed in power systems to stabilize frequency fluctuation and guarantee power quality. However, most existing LFC methods rely on accurate power system modeling and usually ignore the nonlinear characteristics of the system, limiting controllers' performance. To solve these problems, this article proposes a model-free LFC method for nonlinear power systems based on deep deterministic policy gradient framework. The proposed method establishes an emulator network to emulate power system dynamics. After defining the action-value function, the emulator network is applied for control actions evaluation instead of the critic network. Then, the actor network controller is effectively optimized by estimating the policy gradient based on zeroth-order optimization and backpropagation algorithm. Simulation results and corresponding comparisons demonstrate the designed controller can generate appropriate control actions and has strong adaptability for nonlinear power systems.

This article studies the data-driven distributed vehicle platoon control problem for heterogeneous nonlinear vehicle systems. The heterogeneous nonlinear vehicle systems are first transformed into the linear data model with the increment form. Then, a novel data-driven distributed cooperative controller is devised to accomplish the vehicle platoon control objective, where the stability analysis problem is converted into the feasibility problem with the help of the linear matrix inequality technique. The main advantage of the devised control algorithm is that it only utilizes the position and speed information of neighboring vehicles and does not require any pre-training process. Finally, simulation is given to demonstrate the devised control algorithm with comparisons.

This study concentrates on the finite-time protocol-based stabilization for networked unmanned marine vehicle (UMV) systems with denial-of-service (DoS) attacks. To save limited communication resources, a novel event-driven protocol is proposed, in which Q-learning mechanism is adopted to adjust the threshold of the driven condition intelligently. In light of aperiodic DoS attacks, the corresponding switched system is constructed in light of the average dwell time strategy. Employing switching Lyapunov functional, sufficient conditions are made for finite-time boundedness of the UMV systems with disturbance, respectively, in which the relationship among the DoS parameter, the triggered parameter, the finite-time parameter, and the sampling period is accurately characterized. Moreover, the co-design approach of driven parameter and controller gain is proposed for the corresponding UMV systems. Finally, the benchmark UMV systems are shown to verify the effectiveness of the proposed strategy.

As an important device for high-speed railway (HSR) to obtain electrical energy from outside, the structure of pantograph will directly affect the safety and stability of HSR operation. The current pantograph structure detection algorithm has a low accuracy rate, which cannot effectively cope with various complex scenarios and external disturbances during the actual operation of HSR and is difficult to achieve real-time detection of the pantograph structure. In order to solve the current problems in pantograph structure detection, the rigid target subregion longest common sub-sequence (RTS-LCSS) is proposed, which is a new method for real-time pantograph structure detection. This paper realizes the positioning of pantograph region by you only look once (YOLO) V7, then the pantograph characteristic curve is fitted according to the pantograph feature points, and finally compares the fitted pantograph characteristic curve with the normal pantograph characteristic curve to achieve the accurate evaluation of the pantograph structure. Meanwhile, in order to improve the accuracy of YOLO, this paper adopts deep convolution generative adversarial nets (DCGAN) to expand the training samples in conjunction with the real situation of HSR. The method proposed in this paper can realize the real-time detection of pantograph structure in addition to the specific localization of structural abnormal regions. The experimental results show that the proposed method has higher robustness and stronger environmental adaptation. The algorithm accuracy and real-time performance meet the actual operation requirements of HSR, and it continues to function well under the influence of complex scenes and external environment interference.

This paper proposes a fault-tolerant and intrusion-tolerant control strategy for a two-dimensional (2-D) planar vehicular platoon system subject to unknown limitless reversals in fault directions (ULRFDs) and stochastic false data injection attacks (FDIAs). An algorithm is also proposed to realize some actual traffic scenarios such as multi-lane vehicle merging and a single vehicle joining or exiting a platoon. In contrast to the existing results, under stochastic FDIAs, the considered fault directions can be unknown, time-varying, and limitless continuously transformed, as well as the fault frequency is not limited. A novel Nussbaum function with a bounded and adjustable amplitude is constructed using the idea of time-elongation (instead of amplitude-elongation) to attenuate the control input shocks caused by the amplitude-elongation Nussbaum function and to solve the ULRFD problem effectively. Furthermore, two Zeno-free event-triggered mechanisms (ETMs) respectively for velocity and angular velocity are constructed to reduce the communication cost on the controller-actuator channels. It is worth mentioning that a passive intrusion-tolerant method without introducing any additional learning parameter is adopted to solve stochastic FDIAs on the controller-actuator channels. This simplifies our controller structure and reduces the online computational load. Finally, simulation results validate the effectiveness and supremacy of the proposed control strategy and algorithm.

In this paper, the distributed coordination control of path tracking and nash equilibrium seeking of networked automated ground vehicles systems with unknown dynamics is investigated under the framework of graphical games. Different from existing works assuming that the vehicle dynamics are known, each vehicle with completely unknown system dynamics is considered in this paper. To solve this problem, a learning-based data-driven technique is proposed to identify and reconstruct the unknown system matrices. Then, based on the identified system matrices, an offline reinforcement learning (RL) algorithm is proposed to derive both the optimal control policies and the policy iteration solution for graphical games, as well as its corresponding convergence is analyzed. Besides, an online learning algorithm only relying on the online information of states and inputs in an online way is developed to solve the optimal path tracking control problem. As a result, the requirement of relying on the vehicle’s dynamics in the traditional tracking control protocols is completely relaxed by our proposed method. The optimal distributed control policies found by the proposed RL algorithm satisfies the global Nash equilibrium and synchronizes all tracked vehicles to the pinning vehicle. Numerical simulation results are provided to show the effectiveness of the theoretical analysis.

In this article, we consider the cooperative tracking problem for a class of nonlinear multiagent systems (MASs) with unknown dynamics under denial-of-service (DoS) attacks. To solve such a problem, a hierarchical cooperative resilient learning method, which involves a distributed resilient observer and a decentralized learning controller, is introduced in this article. Due to the existence of communication layers in the hierarchical control architecture, it may lead to communication delays and DoS attacks. Motivated by this consideration, a resilient model-free adaptive control (MFAC) method is developed to withstand the influence of communication delays and DoS attacks. First, a virtual reference signal is designed for each agent to estimate the time-varying reference signal under DoS attacks. To facilitate the tracking of each agent, the virtual reference signal is discretized. Then, a decentralized MFAC algorithm is designed for each agent such that each agent can track the reference signal by only using the obtained local information. Finally, a simulation example is proposed to verify the effectiveness of the developed method.

Enabled by the advances in communication networks, computational units, and control systems, cyber-physical power systems (CPPSs) are anticipated to be complex and smart systems in which a large amount of data are generated, exchanged, and processed for various purposes. Due to these strong interactions, CPPSs will introduce new security vulnerabilities. To ensure secure operation and control of CPPSs, it is essential to detect the locations of the attacked measurements and remove the state bias caused by malicious cyber-attacks such as false data inject attack, jamming attack, denial of service attack, or hybrid attack. Accordingly, this article makes the first contribution concerning the representation-learning-based convolutional neural network (RL-CNN) for intelligent attack localization and system recovery of CPPSs. In the proposed method, the cyber-attacks' locational detection problem is formulated as a multilabel classification problem for CPPSs. An RL-CNN is originally adopted as the multilabel classifier to explore and exploit the implicit information of measurements. By comparing with previous multilabel classifiers, the RL-CNN improves the performance of attack localization for complex CPPSs. Then, to automatically filter out the cyber-attacks for system recovery, a mean-squared estimator is used to handle the difficulty in state estimation with the removal of contaminated measurements. In this scheme, prior knowledge of the system state is obtained based on the outputs of the stochastic power flow or historical measurements. The extensive simulation results in three IEEE bus systems show that the proposed method is able to provide high accuracy for attack localization and perform automatic attack filtering for system recovery under various cyber-attacks.

This work investigates the protocol-based synchronization of inertial neural networks (INNs) with stochastic semi-Markovian jumping parameters and image encryption application. The semi-Markovian jumping process is adopted to characterize INNs under sudden complex changes. To conserve the limited available network bandwidth, an adaptive event-driven protocol (AEDP) is developed in the corresponding semi-Markovian jumping INNs (S-MJINNs), which not only reduces the amount of data transmission but also avoids the Zeno phenomenon. The objective is to construct an adaptive event-driven controller so that the drive and response systems maintain synchronous relationships. Based on the appropriate Lyapunov functional, integral inequality, and free weighting matrix, novel criteria are derived to realize the synchronization. Moreover, the desired adaptive event-driven controller is designed under a semi-Markovian jumping process. The proposed method is demonstrated through a numerical example and an image encryption process.

This article considers the output tracking control problem of nonidentical linear multiagent systems (MASs) using a model-free reinforcement learning (RL) algorithm, where partial followers have no prior knowledge of the leader's information. To lower the communication and computing burden among agents, an event-driven adaptive distributed observer is proposed to predict the leader's system matrix and state, which consists of the estimated value of relative states governed by an edge-based predictor. Meanwhile, the integral input-based triggering condition is exploited to decide whether to transmit its private control input to its neighbors. Then, an RL-based state feedback controller for each agent is developed to solve the output tracking control problem, which is further converted into the optimal control problem by introducing a discounted performance function. Inhomogeneous algebraic Riccati equations (AREs) are derived to obtain the optimal solution of AREs. An off-policy RL algorithm is used to learn the solution of inhomogeneous AREs online without requiring any knowledge of the system dynamics. Rigorous analysis shows that under the proposed event-driven adaptive observer mechanism and RL algorithm, all followers are able to synchronize the leader's output asymptotically. Finally, a numerical simulation is demonstrated to verify the proposed approach in theory.

In this article, a data-efficient off-policy reinforcement learning (RL) approach is proposed for distributed output tracking control of heterogeneous multiagent systems (HMASs) using approximate dynamic programming (ADP). Different from existing results that the kinematic model of the exosystem is addressable to partial or all agents, the dynamics of the exosystem are assumed to be completely unknown for all agents in this article. To solve this difficulty, an identifiable algorithm using the experience-replay method is designed for each agent to identify the system matrices of the novel reference model instead of the original exosystem. Then, an output-based distributed adaptive output observer is proposed to provide the estimations of the leader, and the proposed observer not only has a low dimension and less data transmission among agents but also is implemented in a fully distributed way. Besides, a data-efficient RL algorithm is given to design the optimal controller offline along with the system trajectories without solving output regulator equations. An ADP approach is developed to iteratively solve game algebraic Riccati equations (GAREs) using online information of state and input in an online way, which relaxes the requirement of knowing prior knowledge of agents' system matrices in an offline way. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis.

Unknown distributed incipient thermal fault detection and localization are vital to the safe operation of batteries while they have not been given sufficient attention in existing works compared to the studies on estimation of State of Charge (SoC) as well as State of Health (SoH). In order to fill this gap, a backstepping-based fault localization filter (FLF) is presented. Generally, full-state temperature measurement is required to achieve fault localization, which is impossible in applications. However, with the help of interpolation-based approximation, the required number of sensors decreases from infinity to only a few, which guarantees the usability of FLF. A comprehensive methodology framework, including the FLF design, residual evaluation in a distributed manner, and threshold computation, is introduced to guarantee reliable and robust performance in a real-time pattern. Theoretic analysis as well as experiment validations are presented to for validation.

This article is concerned with the observer-based asynchronous control for discrete hidden semi-Markov switching power systems under random denial-of-service (DoS) attacks. Considering the mismatched behavior between the controller and the system, the designed controller based on the observer model runs asynchronously with the system. The hidden stochastic switching model is introduced to characterize this mismatched behavior. Due to the difficulty in obtaining complete information about the semi-Markov kernel (SMK) in practice, the elements in the SMK of the underlying system associated with the hidden mode are considered to be incompletely known. Next, regarding the random DoS attacks and incomplete SMK, the conditions on the existence of the asynchronous controller based on the observer model are proposed by employing the stochastic Lyapunov function, and the closed-loop system is guaranteed to be mean-square stable. Finally, the effectiveness of the proposed scheme is validated through an example.

In this article, we focus on the asynchronous$\mathscr {H}_{\infty }$control problem for discrete-time hidden Markov jump systems (MJSs) with complex mode transition probabilities (C-TPs). The significance of this study can be revealed as follows. First, a hidden Markov model (HMM) is used to estimate the system mode, which cannot always be accurately detected in practical scenarios. Second, the TPs of the Markov process are commonly difficult to be precisely obtained in practical scenarios; hence, some TPs of the Markov process could be unknown or imprecise, resulting in the C-TPs circumstances. Particularly, when taking the HMM into consideration, the C-TPs can also appear in the conditional TPs of the joint process. Therefore, in this work, we consider that the C-TPs may simultaneously exist in both processes of the HMM, which is more practical for hidden MJSs. Additionally, the established results can cover some special cases, for instance, the cases in which C-TPs only exist in one of the processes or neither. The Markov and the joint processes of the HMM are separated by a mode separation strategy so that the C-TPs of them can be, respectively, addressed. Furthermore, only necessary conservatism in dealing with the C-TPs is further introduced. Specifically, when the TPs are perfectly known, the established results can be reduced to an existing result without increasing conservatism. Two examples are presented to demonstrate the effectiveness of the developed results.

Finite-time observability of switching Boolean networks (SBNs) with Markov jump parameters (MJPs) is studied in this article. Via a parallel extension method, the observability of the considered SBN with MJPs is equivalent to that zero vector is reachable from an initial set of the new constructed system. A necessary and sufficient condition based on the extended structure matrix is presented for finite-time observability. Further, for unobservable systems, mode-dependent pinning control is first introduced and applied to achieve the observability. After the set of pinning subsystems is selected, for each pinning subsystem, mode-dependent pinning nodes, output-feedback controls (OFCs), and the adding approaches are designed. An algorithm is provided to find the set of pinning subsystems. Moreover, a necessary condition is given to solve mode-dependent pinning nodes, and the solvability of mode-dependent OFCs and the adding approaches are guaranteed. Finally, a numerical example is presented to show the effectiveness of the obtained results.

First, state estimation for Boolean networks (BNs) with Markov jump parameters (MJPs) is studied in this article. Using semi-tensor product of matrices, the algebraic form of the considered BN with MJPs is constructed. State estimation and mode estimation algorithms based on the output feedback values are presented respectively for the two cases where the output of the observer is deterministic and contains perturbation. Precisely, a recursive matrix-based algorithm, Algorithm 1, is presented to predict the forward state based on minimizing the mean square error. Further, with the help of Bayes Theorem, the optimal system mode estimation is solved and Algorithm 2 is presented to show how to estimate the optimal one from all candidate modes. Finally, a BN with MJPs is constructed from network${\rm p53-MDM2}$and the simulation process shows that the results obtained in this article is effective.

This article presents a resilient formation control framework for networked nonholonomic mobile robots (NMRs) that enables long-time recovery abilities subject to denial-of-service (DoS) attacks by taking advantage of the Koopman operator. Due to the intermittent interruption of communication under DoS, the transmitted signals among the networked NMRs are incomplete. In the lifted space, the infinite-dimensional Koopman operator is employed to capture a linear characteristic of the missed signals from the available signals. Specifically, a data-driven cost function is developed to approximate the infinite-dimensional Koopman operator, allowing long-term recovery capabilities for the missed signals, where the useful historical data is identified by an event-triggered mechanism (ETM). Then, the least-squares method is implemented to calculate a finite-dimensional approximation of the Koopman operator. Once DoS attacks are active, the missed signals are recovered forward from the latest received signals through the approximation Koopman operator. Furthermore, according to the recovered and transmitted signals, the resilient formation controller with a variable gain takes into account the convergence rate and the steady state formation error. The Lyapunov theorem is introduced to prove that the formation error quickly converges to the minor compact set. A distributed DoS attack example is conducted to validate the efficiency and superiority in numerical simulation, and the proposed method is implemented on the real networked NMRs.

Load frequency control (LFC) is crucial for the economic operation and safety of power systems. Therefore this paper addresses the LFC problem for uncertain multi-area power systems with actuator failures. Specifically, actuator failures, uncertainties and communication bandwidth constraints appearing in multi-area power systems are taken into account simultaneously, and novel reliable event-triggered LFC schemes are proposed to cope with these troubles. The proposed schemes can ensure the asymptotical stability of the closed-loop system when only matched uncertainty exists. For the case of coexisting matched and mismatched uncertainties, the state trajectories of the closed-loop system can be controlled within a bounded set, where the size of the bounded set is only related to the mismatched uncertainty. To illustrate the theoretical results, a numerical example of three-area interconnected power system is presented. Note to Practitioners—Load frequency directly affects the quality of electric energy and is one of the main observation states of power systems, hence LFC has been widely investigated in the literature. For multi-area power systems, the system model to be controlled may be subjected to multiple unfavorable factors in practical situations, such as limited bandwidths, model uncertainties and actuator failures. To cope with these unfavorable factors, this paper is devoted to developing a unified control framework to guarantee the stability of the frequency deviation based on the event-triggered mechanism. Considering both matched and unmatched system uncertainties may exist as well as the bound of system uncertainties can be unknown, event-triggered control schemes including static event-triggered LFC and adaptive event-triggered LFC are accordingly designed to deal with aforementioned situations such that the closed-loop system is asymptotically or boundedly stable. The research outcome of this paper provides simple but effective LFC approaches that can be used to maintain the reliable and stable operation of multi-area power systems.

The problem of the model-free adaptive resilient control (MFARC) for nonlinear cyber-physical systems (CPSs) suffered from aperiodic jamming attacks is investigated in this article. First, the MFARC framework subject to aperiodic jamming attacks is established, and an intermediate variable method is introduced to avoid using the unavailable time-varying parameter and further eliminate an extra assumption on the sign limit of it. Then, a MFARC scheme is devised to track the desired output, where the problem of the tracking control can be transformed into solving a feasibility problem, and the controller parameters can be obtained with the aid of the linear matrix inequality technique. What is more, a novel attack compensation mechanism is developed in the MFARC scheme to mitigate the impact of aperiodic jamming attacks. In the last, an example is provided to verify the effectiveness of the devised MFARC scheme.

This study focused on the asynchronous event-triggered output-feedback controller design problem for discrete-time singular Markov jump systems (MJSs). A hidden Markov model (HMM) was employed to estimate the system mode, which cannot always be ideally detected in practice. Because the full state is also difficult to obtain in practical scenarios, an output-feedback control scheme was used. In addition, an HMM-based event-triggered mechanism was also employed in the design of the controller to reduce the communication burden of the networked system. Sufficient conditions for the stochastic admissibility of a closed-loop singular MJS with a prescribed$H_{\infty }$performance index were established using the Lyapunov functional technique. Finally, design procedures for an asynchronous event-triggered controller were summarized as a linear-matrix-inequality-based optimization algorithm. Two examples were considered to verify the effectiveness of the asynchronous event-triggered output-feedback controller design method.

In this article, we address the asynchronous$H_{\infty }$control problem of a class of hidden Markov jump systems (HMJSs) subject to actuator saturation in the continuous-time domain. A bunch of convex hulls is utilized to represent the saturated nonlinearity. Considering that there is an asynchronous mode mismatch between the system and the controller, we establish a hidden Markov model (HMM) to simulate the situation. By means of the Lyapunov theory, sufficient conditions are presented to ensure that the resultant closed-loop HMJS is stochastically mean square stable within the domain of attraction with a prescribed$H_{\infty }$performance index. Furthermore, the state feedback gain matrix and the estimation of the domain of attraction are given by solving an optimization problem, which is constructed via linear matrix inequality (LMI) techniques. Finally, the reliability and validity of the derived results are examined by a numerical example.

For a class of nonstrict-feedback stochastic nonlinear systems with the injection and deception attacks, this article explores the problem of adaptive neural network (NN) fixed-time control ground on the self-triggered mechanism in a pioneering way. After developing the self-triggered mechanism and the delay-error-dependence function, a neural adaptive delay-constrained fault-tolerant controller is proposed by employing the backstepping technique. The self-triggered mechanism does not require an additional observer to determine the time of the data transmission, which reduces the consumption of the system resources more efficiently. In addition, the whole Lyapunov function with the delay-error-dependence term is developed to solve the deferred output constraint problem. Under the proposed controller, it can be proven that all the signals within the closed-loop system are semiglobally uniformly bounded in probability, while the convergence time is independent of the initial state and the deferred output constraint control performance is achieved. The feasibility and the superiority of the proposed control strategy are shown by some simulations.

In this article, the$l_{1}$-induced performance of the stochastic switched Boolean control network (BCN) is investigated. The switched signal is considered to follow a time-varying probability distribution, the switching of which is considered to have a random dwell time. The asynchronous state feedback control (SFC) is studied to achieve the control objective. This kind of control can avoid the failure of the control due to the inconsistency between the system mode and the control mode, so the results obtained are more general. Using the semitensor product of matrices, the algebraic form of the considered BCN is represented. Under this framework, sufficient conditions are obtained to ensure that the closed-loop system is stochastic stabilized with a prescribed$l_{1}$-induced performance level$\gamma $. Parameters can be solved by inequalities. In addition, when the dwell time converges to infinity, the probability distribution of the switched signal becomes fixed. Necessary and sufficient conditions are presented to ensure the stabilization of the closed system under asynchronous SFC as well as the design of the asynchronous SFC. Then, sufficient condition is obtained for the prescribed$l_{1}$-induced performance level. Examples are presented to show the effectiveness of the obtained results.

This article investigates the event-triggered distributed average tracking (ETDAT) control problems for the Lipschitz-type nonlinear multiagent systems with bounded time-varying reference signals. By using the state-dependent gain design approach and event-triggered mechanism, two types of ETDAT algorithms called: 1) static and 2) adaptive-gain ETDAT algorithms are developed. It is the first time to introduce the event-triggered strategy into DAT control algorithms and investigate the ETDAT problem for multiagent systems with Lipschitz nonlinearities, which is more practical in real physical systems and can better meet the needs of practical engineering applications. Besides, the adaptive-gain ETDAT algorithms do not need any global information of the network topology and are fully distributed. Finally, a simulation example of the Watts-Strogatz small-world network is presented to illustrate the effectiveness of the adaptive-gain ETDAT algorithms.

This work deals with the dynamic-memory event-triggered-based load frequency control issue for interconnected multiarea power systems (IMAPSs) associated with random abrupt variations and deception attacks. To facilitate the transient faults, a semi-Markov process is addressed to model the dynamic behavior of IMAPSs. In order to modulate transmission frequency, a novel area-dependent dynamic-memory event-triggered protocol (DMETP) is scheduled by resorting to a set of the historically released packets (HRPs), which ensures better dynamic performance. From the viewpoint of the defender, the randomly occurring deception attack is taken into account, which is regulated by a Bernoulli-distributed scalar. Benefitting from the DMETP scheduling, a novel framework of the memory-based asynchronous control strategy is formulated, in which the hidden semi-Markov model is adopted to reveal the mode mismatches. Based on the Lyapunov theory, sufficient conditions are established to ensure the stochastic stability of the resulting systems. In the end, the simulation result is presented to reveal the efficiency of the proposed dynamic-memory event-triggered-based approach.

This article considers the security-based passivity problem for a class of discrete-time Markov jump systems in the presence of deception attacks, where the deception attacks aim to change the transmitted signal. Considering the impact of deception attacks on network disruption, it causes the existence of time-varying delays in signal transmission inevitably, which makes the controlled system and the controller work asynchronously. The asynchronous control method is employed to overcome the nonsynchronous phenomenon between the system mode and controller mode. On the other hand, to reduce the frequency of data transmission, a resilient asynchronous event-triggered control scheme taking deception attacks into account is designed to save communication resources, and the proposed controller can cover some existing ones as special examples. Moreover, different triggering conditions corresponding to different jumping modes are developed to decide whether state signals should be transferred. A new stability criterion is derived to ensure the passivity of the resultant system although there exist deception attacks. Finally, a simulation example is given to verify the theoretical analysis.

This article studies the optimized fuzzy prescribed performance control problem for nonlinear nonstrict-feedback systems under denial-of-service (DoS) attacks. A fuzzy estimator is delicately designed to model the immeasurable system states in the presence of DoS attacks. To achieve the preset tracking performance, a simper prescribed performance error transformation is constructed considering the characteristics of DoS attacks, which helps obtain a novel Hamilton-Jacobi-Bellman equation to derive the optimized prescribed performance controller. Furthermore, the fuzzy-logic system, combined with the reinforcement learning (RL) technique, is employed to approximate the unknown nonlinearity existing in the prescribed performance controller design process. An optimized adaptive fuzzy security control law is then proposed for the considered nonlinear nonstrict-feedback systems subject to DoS attacks. Through the Lyapunov stability analysis, the tracking error is proved to approach the predefined region by the preset finite time, even in the presence of DoS attacks. Meanwhile, the consumed control resources are minimized due to the RL-based optimized algorithm. Finally, an actual example with comparisons verifies the effectiveness of the proposed control algorithm.

In this article, a novel dynamic event-triggered protocol is constructed to deal with the filtering problem for affine systems presented by Takagi–Sugeno (T–S) fuzzy model. Unlike the traditional fuzzy systems, a unified framework of T–S fuzzy affine systems is formulated, which is more capable of approximation over different operating regions. For achieving desirable performance while saving communication resources, the memory-based dynamic event-triggered protocol is constructed by flexibly exploiting a series of historically triggered packets. The fading channel is time-varying and described as a novel nonhomogeneous Markov process, in which a higher level deterministic switching signal regulates the variation of transition probabilities. By adopting the region-dependent Lyapunov theory, the mean-square exponentially stable, and expected$H_{\infty }$performance for resulting systems is guaranteed. In the end, an inverted pendulum model is applied to verify the results of the theoretical analysis.

This article focuses on the problem of resilient leader-following consensus for multiagent systems against denial-of-service (DoS) attacks. Two update strategies based on event/self-triggering are proposed for the control protocols to handle the leader-following consensus in unreliable shared networks. We first propose a dynamic event-triggered communication scheme to mitigate unnecessary information transfer through energy-limited and vulnerable networks. Continuous communication between agents can be avoided. DoS attacks are supposed to be aperiodic and asynchronous at different edges. The concept of valid DoS attack interval is introduced, and the frequency and duration of attacks are analyzed. The leader-following consensus can be achieved when there exist DoS attacks using the dynamic event-based control strategy. Then, to avoid continuous event detection and save computation resources, a self-triggered communication function is developed. The next triggering moment is predetermined with the latest received state information, and the Zeno behavior is eliminated for the feasibility of the event/self-triggering scheme. Finally, a simulation is provided to verify the effectiveness of the proposed update strategies and control protocols.

This paper investigates the resilient distributed secure output path following control problem of heterogeneous autonomous ground vehicles (AGVs) subject to cyber attacks based on reinforcement learning algorithm. Most existing results are subject to the same attack models for all communication channels, however multiple channels launched by different attackers are considered in this paper. First, a predictor-acknowledgement clock algorithm for each vehicle is proposed to judge whether the communication channel among neighboring vehicles is attacked or not by receiving or transmitting an acknowledgement. Then, a resilient distributed predictor is proposed to predict the pinning vehicle’s state for each vehicle. In addition, a resilient local control protocol consisting of the feedforward state provided by the predictor and the local feedback state of each vehicle is developed for the output path following problem, which is further converted to the optimal control problem by designing a discounted performance function. Discounted algebraic Riccati equations (AREs) are derived to address the optimal control problem. An off-policy reinforcement learning (RL) algorithm is put forward to learn the solution of discounted AREs online without any prior knowledge of vehicles’ dynamics. It is shown that the RL-based output path following control problem of AGVs imposed by cyber attacks can be achieved in an optimal manner. Finally, a numerical example is provided to verify the effectiveness of theoretical analysis.

This article investigates the asynchronous proportional-integral observer (PIO) design issue for singularly perturbed complex networks (SPCNs) subject to cyberattacks. The switching topology of SPCNs is regulated by a nonhomogeneous Markov switching process, whose time-varying transition probabilities are polytope structured. Besides, the multiple scalar Winner processes are applied to character the stochastic disturbances of the inner linking strengths. Two mutually independent Bernoulli stochastic variables are exploited to characterize the random occurrences of cyberattacks. In a practical viewpoint, by resorting to the hidden nonhomogeneous Markov model, an asynchronous PIO is formulated. Under such a framework, by applying the Lyapunov theory, sufficient conditions are established such that the augmented dynamic is mean-square exponentially ultimately bounded. Finally, the effectiveness of the theoretical results is verified by two numerical simulations.

This article addresses the distributed model-free adaptive control (DMFAC) problem for learning nonlinear multiagent systems (MASs) subjected to denial-of-service (DoS) attacks. An improved dynamic linearization method is proposed to obtain an equivalent linear data model for learning systems. To alleviate the influence of DoS attacks, an attack compensation mechanism is developed. Based on the equivalent linear data model and the attack compensation mechanism, a novel learning-based DMFAC algorithm is developed to resist DoS attacks, which provides a unified framework to solve the leaderless consensus control, the leader-following consensus control, and the containment control problems. Finally, simulation examples are shown to illustrate the effectiveness of the developed DMFAC algorithm.

The problem of the observer-based fully distributed containment control for multiagent systems (MASs) subject to denial-of-service (DoS) attacks is investigated in this article. First, a switched fully distributed control framework is established for a class of DoS attacks constrained by the attack duration. Then, a novel attack-resilient control scheme is developed to accomplish the containment control task. The major advantages of the devised control scheme are that any information of the whole network topology structure is not involved and only the information from neighbor agents is used. What is more, a novel observer-based attack compensator is devised to resist DoS attacks. Finally, a practical example of the mobile robot system is presented to testify the validity of the designed control scheme by a comparison.

This article investigates the sliding mode control (SMC) for discrete-time nonlinear semi-Markov jump models with a partly known semi-Markov kernel (SMK). The nonlinear system is characterized by the Takagi–Sugeno (T–S) fuzzy model, where the membership functions for fuzzy rules are designed to be related to the system mode. In view of the fact that the statistical characteristic of the SMK is difficult to fully obtain in practical engineering, the SMK is recognized to be partly known with less conservativeness than both semi-Markov jump models with completely known SMK and Markov jump models with partly known transition probabilities. On the basis of classical Lyapunov stability and fuzzy-model-based approach, novel convex mean-square stability is proposed for the underlying system by eliminating the nonlinear coupling terms with the aid of additional matrix variables. Afterward, a fuzzy SMC law strategy is constructed to guarantee the reachability of the discrete quasi-sliding mode. Finally, a robot arm model is simulated to verify the proposed fuzzy SMC strategy.

This article investigates fault detection (FD) for discrete-time T–S fuzzy systems via an iterative interval estimation method. By means of system output and the iterative estimation of unknown disturbances, two iterative subsystems are employed to establish iterative state reconstruction free of faults. Resorting to a structure separation technique and the$H_{\infty }$requirement imposed on estimated errors, a sufficient condition is formulated in terms of linear matrix inequality to guarantee the asymptotically stability of the error systems. With the help of the zonotope reachability technique, the state interval without faults consideration is rebuilt in terms of the error boundary. Subsequently, an FD scheme is proposed by checking residual signals whether exceed the residual interval generated from the established error interval. Simulation comparison is provided to verify the validity of the proposed iterative FD scheme.

Pinning control is applied to ensure the stabilization of Boolean networks (BNs) with time delay parameter. The time delay parameter in this article follows an independent identical distribution. Using semi-tensor product of matrices, the considered BN with time delay is converted to a high dimensional switching BN and the switching signal is the time delay signal. Different from general switching BN, the structure matrices of all subsystems are independent with each other, structure matrices of all subsystems for the high dimensional switching BN depend on the original BN structure matrix. Pinning control is designed to guarantee the global stochastic stability of the considered system. Furthermore, the global stochastic stability of BN with time delay parameter is proved to be equivalent to the stability of BN without time delay parameter, which greatly reduces the computational complexity and simplifies the control design. For the considered BN with finite cycles, constrained pinning control is applied with limitation that only one state of each undesired cycle is under control. With this constraint, the minimal number of pinning control nodes is further investigated. Some algorithms are presented to obtain the new structure matrix, with which, pinning control can be solved by the obtained methods. Both numerical examples and biological example illustrate the effectiveness of the results.

This article investigates the prescribed performance security control problem for nonlinear systems subject to denial-of-service (DoS) attacks. An attack compensator is adopted to model the unavailable output signal when the attack is active. Based on the designed attack compensator, a fuzzy estimator is developed to approximate the unmeasurable state variables. Further, a security tracking control method is proposed by combining the fuzzy estimator and the novel attack-dependent barrier Lyapunov function. It can steer the tracking errors to the predetermined neighborhood around the origin in the predefined settling time. Meanwhile, all the closed-loop signals are bounded under DoS attacks. Compared with the existing conclusions with DoS attacks, the tracking accuracy and the time of achieving the tracking can be given in advance. Finally, two comparison simulations verify the validity of the designed security controller.

This article focuses on the secure consensus problem of linear multiagent systems (MASs) with quantized event-triggered control (ETC) against denial-of-service (DoS) attacks. DoS attacks with constrains on frequency and duration are discussed, which intend to block the communication links between agents to destroy the consensus. Due to the limited transmission capacity and communication resources, a uniform quantizer and an event-triggered mechanism (ETM) are considered to economize energy consumption. An ETC protocol based on the quantized relative state is designed to resist malicious DoS attacks. Then, sufficient conditions to guarantee the practical consensus of MASs are derived with finite data transmission rates, and the tolerance of DoS attack frequency and duration are given. The Zeno behavior does not exhibit by proving that lower positive bounds exist for all agents, indicating the feasibility of the proposed ETM. Finally, two simulation examples are given to verify the validity and superiority of the theoretical results.

In this article, we study the adaptive fault-tolerant control (FTC) for multiagent systems (MASs) under denial-of-service (DoS) attacks and actuator faults. The mixed connectivity-maintained/broken attack containing both connectivity-maintained and connectivity-broken attacks is considered, which results that the MASs are with switching topologies. Also, characteristics of DoS attacks, such as durations and frequencies of attacks, make it more difficult to design fault-tolerant controllers and analyze the stability for MASs with node faults. A novel FTC strategy is presented to compensate for node faults. Then, by using the Lyapunov stability analysis, an average dwell-time condition is presented for the bounded synchronization of MASs under DoS attacks. An example of nonlinear forced pendulum systems is proposed to verify the proposed approach.

In the field of machine learning, a machine learning system with multiple nodes is usually used, and each node is used to perform a machine learning distributed training process for a part of the data that is allocated to it and provide a server by performing the machine learning distributed training process. The obtained training result, its machine learning data needs to be transmitted through the network. This paper proposes a link allocation method for distributed machine learning. For machine learning computing nodes distributed across domains, due to inconsistencies in link distance, node performance, and link load, the traffic distribution between computing nodes is unbalanced. Aiming at the complex computing requirements of distributed machine learning, a link pre-allocation method is proposed, which establishes a central server-link-node topology map, integrates link resources, and determines the logical distance of nodes. For the synchronously distributed machine learning training set, preallocate transmission link resources and initiate transmission according to the remaining storage capacity of nodes. In order to improve the network utilization efficiency in the process of machine learning, it can break through the influence of large network transmission delay on the efficiency of distributed machine learning.

This paper focuses on the distributed consensus seeking of multi-agent systems (MASs) with discrete-time control updating and intermittent communications among agents. Compared with existing linearly coupled protocols, a nonlinear coupled Zeno-free event-triggered controller is first proposed, which is further to project the static and dynamic triggering mechanisms exploited by using the deadband control method. Then, the node-based nonlinear coupled adaptive event-triggered controller with online self-tuning of time-varying coupling weight and its corresponding to static and dynamic deadband-based event-triggered mechanisms are designed, respectively. The exploited adaptive event-triggered controller does not rely on any global information of interaction structure and is implemented in a fully distributed fashion. In addition, two dynamic proposals not only cover existing static strategies as special cases, but also show that the minimal inter-execution time of dynamic one is not smaller than that of static one. Theoretical analysis shows that the proposed static and dynamic deadband-based event-triggered mechanisms can not only ensure the average consensus with Zeno-freeness, but also achieve the data reduction of communication and control. Finally, the proposed algorithms applied to circuit implementation are corroborated to prove its practical merits and validity.

This article concentrates on the synchronization of discrete-time persistent dwell-time (PDT) switched bidirectional associative memory inertial neural networks with time-varying delays. Through the use of the switched system theory related to the PDT, the convex optimization technique together with some straightforward decoupling methods, an appropriate mode-dependent controller with nonfragility is developed to acclimatize itself to some practical circumstances. Simultaneously, sufficient conditions of ensuring the$\mathcal {H}_{\infty }$performance and exponential stability for the resulting switched synchronization error system are derived. Finally, a numerical example is utilized to show the validity of the model constructed and the influence of the PDT on the$\mathcal {H}_{\infty }$performance. In addition, an image encryption example is employed to show the potential application prospect of the investigated system.

This article studies the problem of dissipativity-based asynchronous state estimation for a class of discrete-time Markov jump neural networks subject to randomly occurring nonlinearities, sensor saturations, and stochastic parameter uncertainties. First, two stochastic nonlinearities occurring in the system are described by statistical means and obey two Bernoulli processes independently. Then, the hidden Markov model is used to characterize the real communication environment closely between the designed estimator and the system model due to the networked-induced phenomenons that also lead to randomly occurring parametric uncertainties of the estimator considered modeled by two Bernoulli processes. A new criterion is established to guarantee that the resulting error system is stochastically stable with predefined dissipativity performance. Finally, we provide a simulation example to validate the theoretical analysis.

This work investigates the observer-based asynchronous control of discrete-time nonlinear systems with network-induced communication constraints. To avoid the data collisions and side effects in a constrained communication channel, a novel dynamic event-based weighted try-once-discard (DEWTOD) protocol is proposed. In contrast to the existing protocols, the DEWTOD scheduling regulates whether the sampling instant to release and which node to transmit the sampling instant simultaneously. In light of a hidden Markov model, the time-varying detection probability matrix is characterized by a polytopic set. By resorting to the polytopic-structured Lyapunov functional, sufficient conditions are derived such that the closed-loop dynamic is mean-square exponentially stable, and the observer-based controller is designed. In the end, two numerical examples are provided to explicate the validity of the attained methodology.

This article investigates the cooperative output regulation problem for heterogeneous nonlinear multiagent systems subject to disturbances and quantization. The agent dynamics are modeled by the well-known Takagi-Sugeno fuzzy systems. Distributed reference generators are first devised to estimate the state of the exosystem under directed fixed and switching communication graphs, respectively. Then, distributed fuzzy cooperative controllers are designed for individual agents. Via the Lyapunov technique, sufficient conditions are obtained to guarantee the output synchronization of the resulting closed-loop multiagent system. Finally, the viability of proposed design approaches is demonstrated by an example of multiple single-link robot arms.

This article investigates the cooperative output regulation problem for discrete-time heterogeneous multiagent Markov jump systems. Two cases are studied: 1) output regulation quadratic control in the case where the exosystem is accessible to all agents and 2) cooperative output regulation quadratic control in the case where only a part of agents can directly communicate with the exosystem. The hidden Markov models are employed to describe the asynchronous modes of the agents and their corresponding controllers. Via the jumping regulator equation, asynchronous control laws are constructed and the algorithms to obtain control parameters are presented in terms of linear matrix inequalities. For the first case, the optimal synchronous/mode-dependent control law, which is a special case of the asynchronous control protocol, is also given via the stochastic dynamic programming approach. Finally, an example is given to illustrate the effectiveness of the proposed approaches.

This article analyzes the problem of the sliding-mode control (SMC) design for discrete-time piecewise nonhomogeneous Markov jump nonlinear systems (MJNSs) subject to an external disturbance with time-varying transition probabilities (TPs). A discrete-time asynchronous integral sliding surface is constructed, which yields matched-nonlinearity-free sliding-mode dynamics (SMDs). Then, by using the mode-dependent Lyapunov function technique, a sufficient condition is established for ensuring the stochastic stability of SMD with extended dissipation. The solution to designing controller gains is obtained. Moreover, an SMC law and an adaptive law are, respectively, derived for driving the system trajectories to move into a predetermined sliding-mode region with specified precision. Finally, the feasibility and effectiveness of the new design are verified and demonstrated by a simulation example.

In this article, we consider the distributed formation navigation problem of second-order multiagent systems subject to both velocity and input constraints. Both collision avoidance and connectivity maintenance of the network are considered in the controller design. A control barrier function method is employed to achieve multiple control objectives simultaneously while satisfying the velocity and input constraints. First, a nominal distributed leader-following formation controller is proposed which satisfies the velocity and input constraints uniformly and handles switching communication graphs. A nonsmooth analysis is employed to prove the global convergence of the controller. Then, a topology-based connectivity maintenance strategy using a new notion of the formation-guided minimum cost spanning tree is proposed and the corresponding barrier function-based constraints are derived. The barrier function-based collision-avoidance conditions are also developed. All barrier function-based constraints are then combined to formulate a quadratic programming problem which modifies the nominal controller when necessary to achieve both collision avoidance and connectivity maintenance. Simulation results demonstrate the effectiveness of the proposed control strategy.

This article, based on dissipativity theory, aims to tackle the consensus tracking issue for Lipschitz nonlinear singular multiagent systems (MASs) with switching topologies and communication delays. Rooted at the leader node, a directed spanning tree is assumed to be contained in the union of all possible interaction graphs. Within the framework of topology switching controlled by a Markov chain, communication delays encountered in the data transmission process are reasonably considered to be time-varying and dependent on Markovian jump modes. By using tools from the stochastic Lyapunov functional technique, algebraic graph theory, and strict (Q,S,R)-α -dissipativity analysis, the consensus controller collecting delayed in-neighboring agents' information is designed to ensure stochastic admissibility and strict dissipativity of the resulting consensus error system. The theoretical analysis is validated by numerical simulations.

This article is concerned with the robust adaptive fault-tolerant control (FTC) circuit designs for a class of continuous-time disturbed systems. A circuit realization method is investigated to convert the robust adaptive FTC control schemes into analog control circuits. An adaptive compensation control scheme against state-dependent and partially bounded actuator faults and disturbances is first developed to demonstrate the approach clearly, then its equivalent control circuits are implemented by using the circuit theory. Compared with simulation results achieved by MATLAB and professional circuit simulation software, the effectiveness of the proposed robust adaptive FTC circuits is validated by a rocket fairing system and a Chua's circuit system.

This article studies the observer-based event-triggered containment control problem for linear multiagent systems (MASs) under denial-of-service (DoS) attacks. In order to deal with situations where MASs states are unmeasurable, an improved separation method-based observer design method with less conservativeness is proposed to estimate MASs states. To save communication resources and achieve the containment control objective, a novel observer-based event-triggered containment controller design method based on observer states is proposed for MASs under the influence of DoS attacks, which can make the MASs resilient to DoS attacks. In addition, the Zeno behavior can be eliminated effectively by introducing a positive constant into the designed event-triggered mechanism. Finally, a practical example is presented to illustrate the effectiveness of the designed observer and the event-triggered containment controller.

Mode information is of great significance when investigating the Markov jump systems (MJSs). However, it is common in practical scenarios that the mode information is not completely accessible, which probably induces nonsynchronization problems. Taking this into consideration, in this article, we study nonsynchronous$\mathcal H_{\infty }$model order reduction for 2-D MJSs with model uncertainty. The considered 2-D system and reduced-order model are characterized by the Roesser model. The nonsynchronization phenomenon between the original system and the reduced-order model is dealt with under the framework of the hidden Markov model. By appropriately selecting the Lyapunov function, the asymptotic mean-square stability and the$\mathcal H_{\infty }$performance of the error system are analyzed, and sufficient conditions are proposed. Based on this, an efficient design method for nonsynchronous model order reduction is further proposed with the help of a projection lemma. Finally, the correctness and effectiveness of the designed reduced-order model are verified through some simulations.

This article investigates the model predictive control (MPC) for discrete-time Markov jump systems (MJSs). First, the asynchronization between the modes of the controller and those of the plant is studied. An asynchronous MPC controller is designed to tackle this issue. Next, to reduce the computational cost and communication burden, a version of the dynamic event-triggered mechanism (ETM) is presented. Finally, the exogenous disturbances are considered and the notion of mean-square input-to-state stability (ISS) is taken into account in the controller design. The highlight of this article is the introduction of both dynamic ETM and asynchronous control into the MPC design. The control algorithm is developed and formulated as a convex optimization problem. Moreover, the recursive feasibility and the closed-loop mean-square ISS are both studied. Finally, some simulations are given to show the effectiveness of the derived MPC method.

In this article, asynchronous sliding-mode control (SMC) is investigated for 2-D discrete-time Markov jump systems. As the system modes are not always accessible to the controller, the hidden Markov model is employed to describe the asynchronization between the system modes and controller. A new 2-D sliding surface is constructed and the corresponding asynchronous SMC law is designed under the framework of the hidden Markov model. By Lyapunov function and linear matrix inequality (LMI) approaches, the reachability of system dynamics to the predefined sliding surface is investigated, and sufficient conditions are established to guarantee that the underlying 2-D system is asymptotically mean-square stable (AMSS) with an$H_{\infty }$disturbance attenuation performance. Then, an algorithm is provided to derive the asynchronous 2D-SMC law. Finally, an example is given to verify the validity and effectiveness of the new SMC law design algorithm.

Using semitensor products (STPs) of matrices, sampled-data state-feedback control (SDSFC) with the Lebesgue sampling region$\mathcal {S}_{\tau }$is first considered to stabilize a Boolean control network (BCN) to a fixed point, under which a necessary and sufficient condition for stabilization is obtained when the considered BCN with the Lebesgue sampling region is converted to a switching system. Meanwhile, the corresponding asynchronous SDSFC gains are designed from a sequence of reachable sets. Then, an algorithm is shown to obtain the minimal number of controlling times and all states globally stabilize to the desired state with the fastest convergence rate under the minimal number of controlling times. Besides, the results have been extended to$p$Lebesgue sampling regions$\mathcal {S}_{\tau _{i}}, i=1,2,\ldots, p$. And some results are presented for this situation, including the necessary and sufficient conditions stabilization under the$p$Lebesgue sampling regions, the asynchronous SDSFC gains, and the algorithm to obtain the optimal states sampling regions. Examples are listed to show the effectiveness of our results, and the biological example indicates that the SDSFC with Lebesgue sampling is also suitable for stochastic BCNs.

Sampled-data state feedback control with stochastic sampling periods for Boolean control networks (BCNs) is investigated in this article. First, based on the algebraic form of BCNs, stochastic sampled-data state feedback control is applied to stabilize the considered system to a fixed point or a given set. Two kinds of distributions of stochastic sampling periods are considered. First, the distribution of sampling periods is assumed to be independent identically distributed (i.i.d.) in the range of any positive integers and the second distribution of sampling periods is assumed to follow an infinite Markov process. A BCN with infinite stochastic sampling periods proves to be equivalent to a finite stochastic switched system, based on which, necessary and sufficient conditions are given to guarantee the stabilization and set stabilization of the BCN with stochastic sampling periods. For the first one, two algorithms are given to guarantee the stabilization and set stabilization of the considered system. For the second one, necessary and sufficient conditions are all presented in the linear programming form. Examples are listed to show the effectiveness of our results.

This article addresses the adaptive neural tracking control problem for a class of uncertain stochastic nonlinear systems with nonstrict-feedback form and prespecified tracking accuracy. Some radial basis function neural networks (RBF NNs) are used to approximate the unknown continuous functions online, and the desired controller is designed via the adaptive dynamic surface control (DSC) method and the gain suppressing inequality technique. Different from the reported works on uncertain stochastic systems, by combining some non-negative switching functions and dynamic surface method with the nonlinear filter, the design difficulty is overcome, and the control performance is analyzed by employing stochastic Barbalat's lemma. Under the constructed controller, the tracking error converges to the accuracy defined a priori in probability. The simulation results are shown to verify the availability of the presented control scheme.

This article aims at addressing the transient bipartite synchronization problem for cooperative-antagonistic multiagent systems with switching topologies. A distributed iterative learning control protocol is presented for agents by resorting to the local information from their neighbor agents. Through learning from other agents, the control input of each agent is updated iteratively such that the transient bipartite synchronization can be achieved over the targeted finite horizon under the simultaneously structurally balanced signed digraph. To be specific, all agents finally have the same output moduli at each time instant over the desired finite-time interval, which overcomes the influences caused by the antagonisms among agents and topology nonrepetitiveness along the iteration axis. As a counterpart, it is revealed that the stability can be achieved over the targeted finite horizon in the presence of a constantly structurally unbalanced signed digraph. Simulation examples are carried out to demonstrate the effectiveness of the distributed learning results developed among multiple agents.

This article is concerned with the problem of guaranteed cost control for 2-D Markov jump Roesser systems with mismatched modes. The hidden Markov model is introduced to describe the asynchronous phenomenon caused by mismatched modes, and an asynchronous linear state-feedback control law is designed based on this model. With the help of the 2-D Lyapunov function and linear matrix inequality (LMI) techniques, sufficient conditions are established to ensure the asymptotic stability of the concerned system with a bound of the predefined guaranteed cost under three different boundary conditions, respectively. Finally, an algorithm that concludes the design processes of the optimal asynchronous guaranteed cost control law is proposed, and a numerical example is provided to verify its effectiveness.

In this article, the problem of distributed synchronization of networked systems with actuator bias faults is investigated. To effectively use the limited network bandwidth and avoid the requirement of global information, a novel adaptive event-triggered state feedback controller and a dynamic triggering law are designed jointly by employing a projection operator approach. The proposed synchronization scheme is different from existing ones that have focused on designing controllers and triggering laws independently. Besides, our scheme is extended to design an observer-based distributed adaptive event-triggered controller and corresponding dynamic triggering law when the system states are unmeasurable. Theoretical analysis shows that under the two different distributed event-triggered synchronization schemes, the following three results can be obtained: 1) fully distributed synchronization can be achieved without knowing global information associated with the underlying communication topology and node's scale; 2) continuous communication among adjacent nodes can be avoided for both designed controllers and dynamic triggering laws; and 3) exclusion of Zeno phenomenon is shown by contradiction. Finally, the effectiveness of the proposed algorithms is verified through three numerical examples.

In this article, the sliding mode control issue is investigated for a class of discrete-time Takagi–Sugeno fuzzy networked singularly perturbed systems via an observer-based technique. Moreover, to process the measurement output and schedule the transmission sequence for relieving the communication burden, a logarithmic quantizer and a weighted try-once-discard protocol are synthesized, which can further improve the network bandwidth utilization in networked control systems. Based on the fuzzy observer states, a novel fuzzy sliding surface is established by considering the singularly perturbed parameter properly, and we endeavor to synthesize a sliding mode control law such that the reachability of the prescribed sliding surface could be guaranteed. In addition, by virtue of the convex optimization theory and Lyapunov approach, sufficient conditions are developed to guarantee the asymptotic stability of the sliding mode dynamics as well as the error system with an expected$H_{\infty }$performance. Finally, a verification example is presented to illustrate the feasibility and effectivity of the proposed method.

This article discusses the prescribed performance control problem for multiagent systems involving the state triggering and the controller output triggering simultaneously. To successfully apply the backstepping technique in the event-triggered control design, the virtual control signal is constructed by the original system state. Compared with the existing results on the prescribed performance, a new barrier Lyapunov function is developed with considering the characteristics of multiagent systems, and a fuzzy adaptive event-triggered control protocol is proposed by the backstepping procedure. It guarantees that the consensus tracking error converges to a predefined region of the origin in a preset finite time. At the same time, all other closed-loop signals remain bounded without the Zeno behavior. Finally, a simulation example confirms the availability of the developed control scheme with a comparison.

As the next-generation power grids, smart grids are integrated with advanced information and communication technology (ICT) to make the grid more efficient and stable than conventional power systems. Given the mounting cyber-attack threats, these critical ICT systems create great security issues for smart grids. Additionally, the clever attackers have the ability to not only access and monitor the smart grid, but also hack it by launching well-established cyber-attacks. Thus, this article is devoted to understanding the potential stealthy cyber-attack and its countermeasure. First, this article proposes a stealthy sparse cyber-attack model in an ac smart grid by considering both the residual test-based detector and the interval-state-estimation-based detector, which is not considered in previous studies. The design model is formulated as a constrained optimization problem by minimizing the number of contaminated meters, where the characteristics of two types of detector are considered simultaneously as the constraints for the first time. A constrained differential evolution (CDE) is proposed as the solver because the optimization problem is NP-hard. Then, a generalized-cumulative-sum-based detector is developed to detect the proposed cyber-attacks, where a fractional-order state transition matrix is originally introduced into the estimator to describe the dynamics of the power system. Numerical studies illustrate the feasibility of CDE-based stealthy sparse cyber-attacks and the effectiveness of the proposed countermeasure.

This paper is concerned with the intelligent event-triggering-based positioning control of networked unmanned marine vehicle (UMV) systems with hybrid attacks, where the UMV and control station is connected by a communication network and the DoS attack and Deception attack are studied. Firstly, a stochastic switched Takagi-Sugeno (T-S) fuzzy system model is proposed for the networked nonlinear UMV systems subject to aperiodic DoS attack and random Deception attack. Then, a novel asynchronous advantage actor-critic (A3C) learning-based event-triggering approach is introduced to alleviate the communication load. By using the Lyapunov stability theory and switched system analysis method, the mean-square exponential stability condition of the closed-loop system and the design method of observer-based controller are devised. Finally, an example of a networked UMV system is given to verify the effectiveness of the proposed resilient control strategy.

The problem of finite-time adaptive tracking control against event-trigger error is investigated in this article for a type of uncertain nonlinear systems. By fusing the techniques of command filter backstepping technical and event-triggered control (ETC), an adaptive event-triggered design method is proposed to construct the controller, under which the effect of event-triggered error can be compensated completely. Moreover, the proposed controller can increase robustness against uncertainties and event error in the backstepping design framework. In particular, we establish the finite-time convergence condition under which the tracking error asymptotically converges to zero in finite time with the aid of a scaling function. Detailed and rigorous stability proofs are given by making use of the improved finite time stability criterion. Two simulation examples are provided to exhibit the validity of the designed adaptive ETC approach.

This article focuses on the issue of nonstationary filtering for uncertain fuzzy Markov switching affine systems (FMSASs) with quantization effects and deception attacks (DAs). The resulting FMSASs are comprised of Markov switching piecewise-affine systems over a set of operating regions. To characterize the multinetwork-induced constraints, the measurement output is quantized before being transmitted, and a compensation scheme is applied to tackle the quantized measurement output loss intermittently. Meanwhile, the randomly occurring DAs are involved, in which the attack behaviors are identified by the bounded stochastic signals. Differently, to deal with the multinetwork-induced constraints, a novel nonstationary region-dependent affine filter strategy is developed. By resorting to a mode-dependent and region-dependent Lyapunov functional and S-procedure theory, sufficient conditions are elicited such that the filtering error system is mean-square exponentially stable. Finally, the practicability of the derived results is verified by a practical tunnel diode circuit model.

This article is concerned with the cluster consensus control problem for multiagent linear systems with a directed communication topology, where only relative output measurements of neighboring agents are available to each agent. Motivated by the pinning control technique, both static and dynamic intermittent output control strategies are proposed. Using Lyapunov functions, sufficient conditions are developed to ensure cluster consensus with existence-guaranteed control parameters. Both periodic and nonperiodic operations of intermittent controllers are investigated. Finally, the effectiveness of the theoretical results is demonstrated by a simulation example.

Distributed stabilization problem is studied in this article for multiple heterogeneous agents in the uncertain strong–weak competition network with exogenous disturbances, where the agents are modeled by the second-order systems with different nonlinear intrinsic dynamics, and the network uncertainty is characterized by unknown nonzero parameters, which contains three different relationships among agents: 1) cooperation; 2) strong competition; and 3) weak competition. To achieve distributed stabilization, the whole network is first divided into two parts: 1) identifiable part and 2) unidentifiable part, and a new distributed robust integral sign of the error (RISE) controller is designed for each agent, where the selection rules of the corresponding parameters are given. It is shown that the heterogeneous multiagent system (MAS) can achieve distributed stabilization no matter whether the identifiable part is structurally balanced or not. Furthermore, it is proved that the global distributed stabilization is achieved for the heterogeneous agents provided that the partial derivatives of the nonlinear intrinsic dynamics are bounded. Finally, two numerical examples are given to demonstrate the effectiveness of the designed controller.

This article is concerned with the consensus formation control problem of a class of networked quadrotor aircraft partially bounded and state-dependent perturbations. A general distributed consensus error model is first developed to formulate the formation behavior of the networked quadrotor aircraft. Then, by using adaptive techniques, virtual position control strategies are proposed to eliminate the impacts of perturbations, so that the following quadrotor aircraft can boundedly track the desired position trajectory with a satisfying pattern. Furthermore, based on the designed virtual position control strategies, the attitude reference angles are constructed and adaptive attitude control strategies are further designed to guarantee that the attitude angles track the reference angles asymptotically. In terms of the asymptotic tracking results of attitude control systems, the bounded consensus formation results are obtained based on the Lyapunov stability theorem. Numerical simulations are carried out to verify the efficiency of the designed position formation as well as attitude tracking control strategies of the networked quadrotor aircraft.

This article is concerned with the robust adaptive fault-tolerant consensus control and the circuital implementation problems for a class of homogeneous multiagent systems with external disturbances and actuator faults. A robust adaptive consensus control strategy is developed to automatically eliminate the effects of actuator bias and partial loss-of-control-effectiveness faults, and simultaneously specify the$L_{2}$performance of systems. The achievement of exponential consensus of the closed-loop disturbed and faulty multiagent system is provided on the basis of the Lyapunov stability theory. Furthermore, a physical implementation method is developed based on circuit theory to translate the proposed adaptive consensus control strategy into analog circuits. By using a professional tool for circuit simulations, effectiveness of the developed circuits is verified via a multiagent system composed by mobile robots with two independent driving wheels.

This article is concerned with how the effect of time-varying topologies is overcome and the behaviors of cooperative–antagonistic networks (CANs) are identified. An extended leader–follower (ELF) framework is established for CANs, in which each leader is allowed to evolve dynamically due to the communication with its neighbor leaders. It is shown that a new class of bipartite containment fluctuation behaviors emerges in the presence of the ELF framework, regardless of any structure conditions for CANs. In particular, the leaders are clustered into separate groups, each of which can realize the modulus consensus, whereas the followers may not be enabled to converge but fluctuate within the bounded region spanned by all leaders’ states and their symmetric states. A simulation example is provided to demonstrate the effectiveness of the behavior analysis results developed for CANs.

Nonlinear couplings among units (nodes) are ubiquitous in engineering systems including, e.g., radar and sonar systems, which have been ignored in most works. In this article, the problem of distributed synchronization of nonlinear networked systems with nonlinear couplings is studied. Specifically, two kinds of nodes' communication couplings including nonlinear relative and nonlinear absolute state couplings are considered. To reduce the requirements of control and communication among nodes and avoid any global network information, two edge-based fully adaptive event-triggered control protocols based on nonlinear relative and absolute state couplings are proposed by using the projection operator technique, which is followed by design of corresponding dynamic event-triggered mechanisms. The advantages of our proposed dynamic event-triggered strategies show that it can boil down to existing static ones as special examples, and the minimal inter-execution time of the proposed dynamic triggering laws is larger than that of static ones. Theoretical analysis shows that the proposed algorithm not only guarantees fully adaptive Zeno-free synchronization of networked systems without requiring any global information, but also avoids continuous communications among nodes, and considerably reduce the frequency of controller updates. Finally, the practical merits of the proposed algorithms are corroborated using a Chua's circuit network.

This article is concerned with the event-triggered output consensus problem for heterogeneous multiagent systems (MASs) with nonuniform communication delays. Unlike the existing event-triggered consensus results, more general heterogeneous linear MASs and nonuniform communication delays are considered. To reduce communication among subsystems, novel dynamic periodic event-triggered mechanisms are proposed. By using the event-triggered signals at the previous sampling instant, new distributed observers are designed to eliminate asynchronous behavior caused by nonuniform communication delays. Based on the developed observers, the observer error system is converted into a time-delay system with interval time-varying delays. Besides, a controller is designed by using the states of observers. It is shown that the consensus problem can be solved by the proposed method. Finally, an illustrative example is provided to verify the effectiveness of the developed method.

This paper addresses the mean-square leader-follower consensus problem for the multiagent Markov jump linear systems. The leader has the general linear dynamics while the followers are subject to parameter changes modeled by Markov jump. By using the output measurement of the leader, two types of observers, namely, the common observer and the distributed adaptive observer, are first constructed together to estimate the leader state. Then based on the estimated state and the follower self information, two kinds of controllers, namely, the synchronous controller and the asynchronous controller, are designed to achieve the mean-square leader-follower consensus. Finally, the simulation results are given to illustrate the feasibility and effectiveness of the proposed approaches.

This paper studies the modeling and synchronization problems for stochastic complex dynamical networks subject to consecutive packet dropouts. Different from some existing research results, both probability characteristic and upper bound of consecutive packet dropouts are involved in the proposed approach of controller design. First, an error dynamical network with stochastic and bounded delay is established by step-delay method, where the randomness of the bounded delay can be verified later by the probability theory method. A new modeling method is introduced to reflect the probability characteristic of consecutive packet dropouts. Based on the proposed model, some sufficient conditions are proposed under which the error dynamical network is globally exponentially synchronized in the mean square sense. Subsequently, a probability-distribution-dependent controller design procedure is then proposed. Finally, two numerical examples with simulations are provided to validate the analytical results and demonstrate the less conservatism of the proposed model method.

This paper adopts two different approaches, the small-gain technique and the integral quadratic constraints (IQCs), to investigate the synchronization problem of coupled harmonic oscillators (CHOs) via an event-triggered control strategy in a directed graph. First, a novel control protocol is proposed such that every state signal of the CHO decides when to exchange information with its neighbors asynchronously. Then, the resulting closed-loop system based on the designed control protocol is converted into a feedback interconnection of a linear system and a bounded operator, and the stable condition of the feedback interconnection is presented by employing the small-gain technique. In order to better describe the relationship between the input and output, the IQCs theorem is applied to derive the stable condition on the basis of the Kalman-Yakubovich-Popov lemma. Finally, a simulation example is provided to verify the proposed new algorithms.

In this article, the dissipativity-based filtering of the Markovian jump neural networks subject to incomplete measurements and deception attacks is investigated by adopting an event-triggered communication strategy, where the attackers are supposed to occur in a random fashion but obey the Bernoulli distribution. Consider that the information of the system mode is transmitted to the filter over the communication network that is vulnerable to external attacks, which may lead to the undesired performance of the resulting system by injecting malicious information from the attackers. As a result, the filter has difficulty completing information from the original system. Besides, an event-triggered communication mechanism is introduced to reduce the communication frequency between data transmission due to the limited network resources, and different triggering conditions corresponding to different jump modes are developed. Then, based on the above considerations, the sufficient condition is derived to ensure the stochastic stability and dissipativity of the resulting augmented system although the deception attacks and incomplete information exist. A numerical simulated example is provided to verify the theoretical analysis.

This article concentrates on dealing with distributed control problems for second-order signed networks subject to not only cooperative but also antagonistic interactions. A distributed control protocol is proposed based on the nearest neighbor rules, with which necessary and sufficient conditions are developed for consensus of second-order signed networks whose communication topologies are described by strongly connected signed digraphs. Besides, another distributed control protocol in the presence of a communication delay is designed, for which a time margin of the delay can be determined simultaneously. It is shown that under the delay margin condition, necessary and sufficient consensus results can be derived even though second-order signed networks with a communication delay are considered. Simulation examples are included to illustrate the validity of our established consensus results of second-order signed networks.

This article is concerned with the issue of l2- l∞state estimation for nonlinear coupled networks, where the variation of coupling mode is governed by a set of switching signals satisfying a persistent dwell-time property. To solve the problem of data collisions in a constrained communication network, the round-robin protocol, as an important scheduling strategy for orchestrating the transmission order of sensor nodes, is introduced. Redundant channels with signal quantization are used to improve the reliability of data transmission. The main purpose is to determine an estimator that can guarantee the exponential stability in mean square sense and an l2- l∞performance level of the estimation error system. Based on the Lyapunov method, sufficient conditions for the addressed problem are established. The desired estimator gains can be obtained by addressing a convex optimization case. The correctness and availability of the developed approach are finally explained via two illustrative examples.

This paper includes some results of switched Boolean control networks. The switched signals considered in this paper is time variant and it follows a certain probabilistic distribution vector. Given a concept of stabilization with stochastic switched signals, we obtained a necessary and sufficient condition for stabilization. Then a state feedback control depending on switched signals is designed to stabilize the system considered. Later we investigate a case when the switched signal θ(t) is unknown at time t, and what we have is the prediction switched signal ∧θ(t). For a given prediction matrix, a necessary and sufficient condition is given to preserve the stabilization. Except state feedback control, asynchronous pinning control for switched Boolean networks (BNs) is also considered. A necessary and sufficient condition is extended for the stability of BNs with stochastic switched signals. Moreover, algorithms are presented to find the minimal number of pinned nodes based on controlling minimal number of subsystems. Examples are shown to illustrate the effectiveness of the obtained results.

This article studies the security of networked interconnected systems (NISs) subject to cyber-attacks based on a new event-triggered mechanism (ETM). NISs with spatially distributed subsystems are vulnerable to cyber-attacks. With a new concept of security control, attention is focused on designing a novel ETM together with a decentralized output feedback control (DOFC) scheme such that the NIS subject to cyber-attacks is stable in secure sense. Under the proposed ETM, the average data-releasing rate over the whole operating period can be extremely decreased, thereby reducing the burden of network bandwidth, computation, and battery-supply. Moreover, during the system with external disturbance or attack on the communication network, more transmission-events can be generated than other periods. As a result, the desired control performance can be achieved. By using stochastic analysis techniques and Lyapunov stability theory, sufficient conditions are derived to obtain both the controller gains and the parameters of the ETM. Numerical simulation of chemical reactor systems is given to illustrate the advantages and effectiveness of the proposed theories and design techniques.

This paper investigates the controller design problem of networked control systems subject to cyber attacks. A hybrid-triggering communication strategy is employed to save the limited communication resources. State measurements are transmitted over a communication network and may be corrupted by cyber attacks. The aim of this paper is to design a controller for a new closed-loop system model with consideration of randomly occurring cyber attacks and the hybrid-triggering scheme. A stability criterion is obtained for the system stabilization by employing Lyapunov stability theory and stochastic analysis techniques. Moreover, the desired controller gain is derived by resorting to some matrix inequalities. Finally, a numerical example is exploited to demonstrate the usefulness of the proposed scheme.

The dropper is one of the core components of the high speed railway catenary and dropper failure will lead to serious transportation accidents. It is very important to carry out dropper failure and defect detection. There are a large number of droppers installed in the catenary. The dropper images are collected by the high-definition camera installed on the top of the moving catenary inspection vehicles. The image quality and image consistency are poor, and it is very difficult to identify dropper defects automatically. The railway department and companies lack efficient and intelligent detection methods. This article innovatively proposes a multialgorithm fusion image processing technology, and builds a dropper recognition and failure-defect detection model based on a deep learning algorithm and subpixel level dropper defect detection model, and achieves high accuracy dropper failure and defect detection. The detection model based on the Faster R-CNN algorithm is studied to realize the positioning and recognition of the dropper and the failure detection of bending slack and broken dropper. The subpixel level dropper defect detection algorithm is based on the fusion of the image preprocessing, dropper fine positioning algorithm, edge fitting and bending zoom algorithms, the Hough transform algorithm, and so on. These can be used to realize detection of defects, such as microdeformation, dropper-strands loosened, dropper-strands broken, and foreign body adhesions. The test is verified by the catenary images taken from a practical high speed railway. The detection accuracy, real-time performance, and stability of the algorithm meet the needs of inspection and maintenance for a high speed railway.

This article presents a fault-tolerant tracking control strategy for Takagi–Sugeno fuzzy model-based nonlinear systems which combines integral sliding mode control with adaptive control technique. Two common actuator faults: 1) loss of effectiveness and 2) increased bias input, are considered simultaneously. The fuzzy tracking control system is first established by incorporating the integral term of the output tracking error. Then, an appropriate fuzzy integral switching surface is designed such that the corresponding sliding motion only suffers from the unamplified unmatched disturbance. The solution of the nominal tracking controller can be transformed into a to convex optimization problem. In particular, an adaptive fuzzy sliding mode tracking controller is synthesized to ensure the accessibility of the sliding motion despite the effect of actuator faults and unknown disturbances. Finally, the proposed tracking strategy is verified by applying it to the dynamic positioning control of unmanned marine vehicles.

In this paper, we study the stabilization and finite-time stabilization of probabilistic Boolean control networks (PBCNs). A complete family of reachable sets is defined first, based on which, feedback stabilization conditions are obtained. Then a way to find all possible state feedback controllers are presented for the stabilization of PBCNs accordingly. Moreover, it has been stated that the approach in this paper can also be applied to finite-time stabilization via some changes in the construction of set sequence. Finally, an evolutionary networked game is given as an example to illustrate the efficiency of the obtained results.

This paper studies the stability analysis of the input saturated discrete-time switched systems with average dwell-time based on the parametric discrete-time Riccati equation. The state feedback controller and the observer-based output feedback controller are designed to guarantee the exponential stability of the closed-loop system. The proposed method is simple and easy to operate in practice. The designed controllers can be computed easily by solving the parametric discrete-time Lyapunov equation. The main advantages of this paper are that the stability analysis is based on the properties of the saturation function and limited structure information of the actuator saturation and that the designed controllers have good robustness to the structure uncertainty of the input saturation. The simulation results illustrate the effectiveness of the proposed methods.

This paper investigates the problem of dissipativity-based asynchronous fuzzy integral sliding mode control (AFISMC) for nonlinear Markov jump systems represented by Takagi-Sugeno (T-S) models, which are subject to external noise and matched uncertainties. Since modes of original systems cannot be directly obtained, the hidden Markov model is employed to detect mode information. With the detected mode and the parallel distributed compensation approach, a suitable fuzzy integral sliding surface is devised. Then using Lyapunov function, a sufficient condition for the existence of sliding mode controller gains is developed, which can also ensure the stochastic stability of the sliding mode dynamics with a satisfactory dissipative performance. An AFISMC law is proposed to drive system trajectories into the predetermined sliding mode boundary layer in finite time. For the case with unknown bound of uncertainties, an adaptive AFISMC law is developed as well. The studied T-S fuzzy Markov jump systems involve both continuous-time and discrete-time domains. Finally, some simulation results are presented to demonstrate the applicability and effectiveness of the proposed approaches.

In this paper, the problem of asynchronous output feedback control is investigated for a class of Takagi-Sugeno fuzzy switched systems subject to intermittent measurements. The Bernoulli process is employed to model the phenomenon of stochastic intermittent measurements. Based on the hidden Markov model and output measurements, an asynchronous controller is designed. Then, sufficient conditions for the existence of an asynchronous controller are proposed, which ensure the stochastic stability of the closed-loop system with desired extended dissipative performance. Finally, an example is presented to illustrate the effectiveness and advantages of the proposed new design techniques.

This paper investigates the H∞ output consensus problem for multiagent systems with Markov jump and external disturbance in both continuous-time and discrete-time domains. The communication network is directed and fixed with uncertainties. Based on the hidden Markov model, an output feedback controller is constructed. Then, the original system is transformed into a reduced-order system, which features the error dynamics. By using a Lyapunov function, sufficient conditions are developed to ensure that all agents can reach the consensus with the desired H∞ performance in the mean-square sense. Finally, simulation results are presented to illustrate the efficiency of the proposed approaches.

This article investigates the reliable output feedback control problem for networked nonlinear semi-Markov jump systems, in which a control strategy with redundant channels is established to reduce the adverse effect caused by packet dropouts. The actuator faults are fully considered in the setup. On the basis of stochastic analysis theory and fuzzy-model-based method, some criteria are established to guarantee the σ -error mean-square stability for the considered systems. As a consequence, the reliable output feedback controller design method is proposed, which can be utilized to deal with the actuator failures problem effectively. Finally, two illustrative examples are employed to explain the availability of the presented design approach, where the single-link robot arm system model is contained.

In this paper, the stabilization problem of the Boolean network (BN) with stochastic disturbances via pinning control has been investigated. The necessary and sufficient conditions are given for robust stabilization of a BN with stochastic disturbances. Then, pinning control is considered to stabilize a BN with stochastic disturbances. An algorithm is given to obtain a new stable system and the pinning control, including the pinned nodes, control design, and control adding, is also solved. Finding the minimal number of pinned nodes is further analyzed. The necessary and sufficient conditions are obtained for the solvability of pinning control, based on which some matrices sets are constructed which leads to the necessary and sufficient conditions of pinning t nodes. Furthermore, an algorithm is introduced to search the minimal number of pinned nodes and what exactly they are, which will reduce the computational burden. Examples are given to illustrate the efficiency of the obtained results.

This paper is concerned with the problem of asynchronous and reliable filter design with performance constraint for nonlinear Markovian jump systems which are modeled as a kind of Takagi-Sugeno fuzzy switched systems. The nonstationary Markov chain is adopted to represent the asynchronous situation between the designed filter and the considered system. By using the mode-dependent Lyapunov function approach and the relaxation matrix technique, a sufficient condition is proposed to ensure the filtering error system, which is a dual randomly switched system, is stochastically stable and satisfies a given l2-l∞performance index simultaneously. Two different approaches are developed to construct the asynchronous and reliable filter. Owing to the Finsler's lemma, the second approach has fewer decision variables and less conservatism than the first one. Finally, two examples are provided to show the correctness and effectiveness of the proposed methods.

This paper studies the problem of event-triggered secure consensus for multiagent systems subject to periodic energy-limited denial-of-service (DoS) attacks, where DoS attacks usually prevent agent-to-agent data transmission. The DoS attacks are assumed to occur periodically based on the time-sequence way and the period of DoS attacks and the uniform lower bound of the communication areas are predetected by some devices. Based on the above assumptions, an event-based protocol consisting of two different measurements corresponding to leader-followers and follower-follower is presented to schedule communications between agents, which can reduce the update frequency of the controller. Then, the stability of the resultant error system is analyzed to derive sufficient conditions of achieving secure consensus by employing the Lyapunov function and the inductive approach. Besides, positive low bounds on any two consecutive intervals of events generated by individual events are calculated to eliminate "Zeno behavior" under the developed triggering condition and event-triggered protocol. Simulation result is provided to verify the theoretical analysis.

This article investigates the set stabilization of probabilistic Boolean control networks (PBCNs) under sampled-data (SD) state-feedback control within finite and infinite time, respectively. First, the algorithms are, respectively, proposed to find the sampled point set and the largest sampled point control invariant set (SPCIS) of PBCNs by SD state-feedback control. Based on this, a necessary and sufficient criterion is proposed for the global set stabilization of PBCNs by SD state-feedback control within finite time. Moreover, the time-optimal SD state-feedback controller is designed. It is interesting that if the sampled period (SP) is changed, the time of global set stabilization of PBCNs may also change or even the PBCNs cannot achieve set stabilization. Second, a criterion for the global set stabilization of PBCNs by SD state-feedback control within infinite time is obtained. Furthermore, all possible SD state-feedback controllers are obtained by using all the complete families of reachable sets. Finally, three examples are presented to illustrate the effectiveness of the obtained results.

This paper investigates the dissipative filtering problem for a class of discrete-time switched fuzzy systems with missing measurements. The fuzzy plant under consideration incorporates characteristics of Takagi-Sugeno fuzzy systems and switched systems simultaneously. The occurrence of missing measurements is described by a stochastic variable that satisfies the Bernoulli binary distribution, which characterizes the effect of data loss in information transmission between the plant and the filter. Utilizing the Lyapunov function technique, sufficient conditions are developed to ensure that the resultant filtering error system is exponentially stable and strictly dissipative. Two simulation examples are presented to illustrate the validity of the proposed method.

This paper is concerned with the reliable extended passive control problem for semi-Markov jump Takagi-Sugeno (T-S) fuzzy systems based on an event-triggered mechanism (ETM). An asynchronous approach is utilized to cope with the fuzzy-basis-dependent membership functions in networked fuzzy systems. For fear of the waste of communication resources, an ETM to screen the transmitted data is adopted for T-S fuzzy semiMarkov jump systems under consideration. Owing to the use of Lyapunov stability theory and some novel integral inequalities based on auxiliary functions, some sufficient conditions are obtained to guarantee that the resulting closed-loop system not only meets a prescribed mixed H∞and passive performance but also is stochastically stable. Then, by using a skillful matrix decoupling approach, the gains of the fuzzy controller may be expressed specifically. Lastly, the superiority and availability of the developed design method are extensively described and validated via its application to a truck control model.

This article concentrates on the output feedback controller design problem for discrete-time nonlinear switched systems with actuator faults. The Takagi-Sugeno fuzzy model is adopted to approximate the nonlinearity of the plant with a set of local linear models. The persistent dwell-time (DT) switching law, which is more general than DT or average DT switching, is introduced to govern the switching among subsystems. In order to alleviate the effects of actuator failures on system stability and performance, a synthesized fault-tolerant output feedback controller ensuring various performance requirements is designed. Intensive attention is focused on establishing sufficient conditions, which can guarantee the exponential mean-square stability as well as the prescribed extended dissipativity property of the closed-loop system. By virtue of the Lyapunov stability theory and appropriate matrix transformation methods, the desired controller gains can be obtained by solving a convex optimization problem. The developed method is finally applied to address the control issue of a tunnel diode circuit system model to illustrate its efficiency and applicability.

This paper is focused on the static output feedback (SOF) control problem for a class of switched nonlinear systems with actuator faults. By means of the Takagi-Sugeno fuzzy model, the switched nonlinear plant is described by a family of switched fuzzy systems. Considering transmission failures may occur between controller and actuator, a reliable SOF controller against actuator faults is designed. Sufficient conditions are developed to guarantee the existence of the reliable SOF controller. Furthermore, an iterative algorithm is designed to determine the controller gains, which avoids the conservatism brought by the traditional singular value decomposition method. To validate the effectiveness of the proposed approach, a numerical example is exploited and simulation results are also presented.

In this paper, the problems of nonfragile and nonsynchronous synthesis for Bernoulli switched systems are studied. The disturbances in the systems are assumed to have bounded peak in the sense of mean square. With the interval additive gain variations consideration, a nonfragile and nonsynchronous controller is designed subject to a nonsynchronous assumption that the switching signals of the system are not accurately gained for synthesis. A sufficient condition that is dependent on the known sojourn probabilities of switching signals is proposed to ascertain the reachable set of the closed-loop system, which is a hidden Bernoulli model, mean square contained in an ellipsoid-like set. Several nonfragile and nonsynchronous controllers are parameterized to guarantee the corresponding requirements to be satisfied for the closed-loop systems. The effectiveness and advantages of the proposed new design techniques are illustrated by a numerical simulation example.

In this paper, we present a direct multirate adaptive control algorithm that ensures global stabilization of a class of (potentially unstable and invertible) linear time-invariant discrete-time plants of known order and relative degree one. An essential feature of our scheme is that no projections are needed, no appeal is made to the persistence of excitation arguments for the stability proof and it has a better transient performance than existing schemes. The implementation of the controller requires some prior information about its Markov parameters, namely, upper and lower bounds on the systems impulse response. It is directly applicable to plants with interlacing real poles and zeros, i.e., with Cauchy index equal to the plant order, provided the adaptation gain is restricted to be smaller than some value determined by the measure of relative primeness of the pole and zero polynomial. This class contains some practically interesting systems, for instance, resistor-inductor or resistance-capacitance circuits.

The problem of reliable controller design for two-dimensional (2-D) systems subject to extended dissipativity is investigated in this paper. Considering the wide usage of Roesser state-space model, a discrete-time Roesser model is introduced to describe 2-D systems. To reinforce the reliability of the system under consideration, actuator-failure model which may severely degrade performance, or even destabilize the system is introduced. Finally, the sufficient condition for the existence of reliable controller is presented to guarantee the mean square asymptotic stability and 2-D extended dissipativity of the closed-loop system under admissible actuator failures. The gains of 2-D controller are derived by means of the convex optimization method. A simulation result is exploited to verify the effectiveness and merits of the theoretical findings.

This paper addresses the control problem of the switched system with bounded actuators and average dwell-time. We present the switching rules for the designed controllers based on two kinds of parametric algebraic Riccati equation. The exponential stability of the switched system under the average-dwell time is ensured by the designed controllers and the switching rules. The dynamic performance of the control system is enhanced greatly by the proposed method. The simulation results illustrate the effectiveness and the usefulness of the obtained theoretical results.

This paper applies an input-based triggering approach to investigate the secure consensus problem in multiagent systems under denial-of-service (DoS) attacks. The DoS attacks are based on the time-sequence fashion and occur aperiodically in an unknown attack strategy, which can usually damage the control channels executed by an intelligent adversary. A novel event-triggered control scheme on the basis of the relative interagent state is developed under the DoS attacks, by designing a link-based estimator to estimate the relative interagent state between intermitted communication instead of the absolute state. Compared with most of the existing work on the design of the triggering condition related to the state measurement error, the proposed triggering condition is designed based on the control input signal from the view of privacy protection, which can avoid continuous sampling for every agent. Besides, the attack frequency and attack duration of DoS attacks are analyzed and the secure consensus is reachable provided that the attack frequency and attack duration satisfy some certain conditions under the proposed control algorithm. “Zeno phenomenon” does not exhibit by proving that there exist different positive lower bounds corresponding to different link-based triggering conditions. Finally, the effectiveness of the proposed algorithm is verified by a numerical example.

This paper addresses the dissipative asynchronous filtering problem for a class of Takagi-Sugeno fuzzy Markov jump systems in the continuous-time domain. The hidden Markov model is applied to describe the asynchronous situation between the designed filter and the original system. Based on the stochastic Lyapunov function, a sufficient condition is developed to guarantee the stochastic stability of the filtering error systems with a given dissipative performance. Two different methods for the existence of desired filter are established. Due to the Finsler's lemma, the second approach has fewer variables to decide and brings less conservatism than the first one. Finally, an example is provided to demonstrate the correctness and advantage of the proposed approaches.

This paper considers the problem of asynchronous guaranteed cost control (GCC) for nonlinear Markov jump systems with stochastic quantization. Hidden Markov model is used to describe the nonsynchronous controller and the random quantization phenomenon. Based on Takagi-Sugeno fuzzy technique and Lyapunov function approach, a sufficient condition is obtained, which can not only ensure the asymptotic stability of the closed-loop system and existence of the desired controller, but also can yield the minimal upper bound of GCC performance. Finally, two examples are provided to demonstrate the correctness and reliability of our developed approaches.

The problem of asynchronous and resilient filtering for discrete-time Markov jump neural networks subject to extended dissipativity is investigated in this paper. The modes of the designed resilient filter are assumed to run asynchronously with the modes of original Markov jump neural networks, which accord well with practical applications and are described through a hidden Markov model. Due to the fluctuation of the filter parameters, a resilient filter taking into account parameter uncertainty is adopted. Being different from the norm-bound type of uncertainty which has been studied in a considerable number of the existing literatures, the interval type of uncertainty is introduced so as to describe uncertain phenomenon more accurately. By means of convex optimal method, the gains of filter are derived to guarantee the stochastic stability and extended dissipativity of the filtering error system under the wave of the filter parameters. Considering the limited computing power of MATLAB solver, a relatively simple simulation is exploited to verify the effectiveness and merits of the theoretical findings where the relationships among optimal performance index, uncertain parameter σ , and asynchronous rate are revealed.

This paper considers the consensus problem of multiagent systems with the input-based triggering condition. A model-based approach is first given to estimate the relative interagent states between intermittent communications instead of absolute states. A novel consensus protocol consisting of the relative interagent states is proposed for the consensus problem. Besides, compared with some results on the triggering condition consisting of state measurement error, a new triggering condition is constructed based on the control input. Then, the consensus protocol is executed by every agent in a fully distributed way, which has the advantage that the controller design is related to the number of agents instead of using global information. Moreover, the bounds of parameters of the controller and the triggering condition can be obtained by the proposed algorithm simultaneously, which depends on the total number of agents in multiagent networks. The proposed control scheme can ensure that states of all agents can achieve consensus. It is shown that “Zeno behavior” does not appear under the proposed algorithm. Finally, an illustrative example is given to verify the proposed method.

A new globally exponentially synchronization criterion for complex dynamical networks is proposed in this paper taking account of discrete time communications units and time delay is considered, in which globally exponential convergence is achieved. By introducing larger decision matrices to gain time-delay synchronization criterion, it not only inherits the benefits of full utilization of available information, but also has less conservatism. At the same time, except for original system parameters, no more new decision variables are increased. A time-dependent Lyapunov functional approach is proposed to prove the effectiveness of the criterion. Some simulation results hold the feasibility of the theory.

In this paper, an asynchronous filter is proposed for Markov jump neural networks (NNs) with time delay and quantized measurements where a logarithmic quantizer is employed. The filter and quantizer are both mode-dependent and their modes are asynchronous with that of the NN, which is described by hidden Markov models. By the Lyapunov-Krasovskii functional approach, a sufficient condition is derived and a filter is then designed such that the filtering error dynamics are stochastically mean square stable and strictly (U, L, V)-dissipative. Finally, the effectiveness and practicability of the theoretical results are verified by two examples, including a biological network.

The problem of state-feedback reliable control against sensor failures for Markov jump nonlinear systems taking into account the mixed time delays is investigated in this paper. To describe the phenomenon of imperfect transmission between the system and the controller, a mode-dependent stochastic measurement fading model is adopted. By introducing two mutually independent and mode-dependent fading channel coefficients, the undulation of communication workload caused by the change of system mode can be accurately described. Also, the mixed time delays including the discrete and the infinite distributed delays are considered. Our goal is to design a reliable controller, which can guarantee the passivity of the closed-loop control system even when some sensors experience faults or failures. To realize this reliable controller, we introduce an additional matrix and utilize some inequality techniques. By using Lyapunov function technique, the gain of the designed controller can be solved. The merits and effectiveness of the developed design scheme are verified by a simulation example.

This paper is concerned with the reliable ℒ2- ℒ∞filter design problem for the nonlinear continuous-time Markov jump systems based on Takagi-Sugeno fuzzy model. A stochastic variable is introduced to describe the encountered sensor failures, the value of which is dependent on the considered plant mode based on a hidden Markov process. In practice, generally the information on plant modes is not fully accessible to the reliable filter, which results in the nonsynchronous phenomena between the modes of involved plant and filter, and has a negative effect on the system performance. A hidden Markov model is also adopted to depict such kinds of nonsynchronous phenomena. The filtering error systems are called fuzzy dual hidden Markov jump systems. A sufficient condition, associated to the modes of the plant, sensor failures, and the filter are proposed for the filtering error systems to ensure the stochastic stability and guaranteed ℒ2- ℒ∞performance, based on which the existence condition and explicit design method of a nonsynchronous filter are both given. Finally, two simulation examples illustrate the effectiveness of the proposed approach.

The asymptotic mean square stability and H2performance of the 2-D hidden Bernoulli jump system is analyzed in this paper. This system, essentially, has a bi-jumping property, in other words, its jumps depend on two jumping parameters, one is the Bernoulli process, the other is dependent on the Bernoulli process via a conditional probability matrix, they together determine which subsystem is active and are coined as hidden Bernoulli model. By means of Lyapunov function method, a sufficient condition is derived, which reveals that the system is asymptotically mean square stable and has a certain H2performance if a set of matrix inequalities are satisfied. Furthermore, the issues of asynchronous control and filtering are addressed for 2-D Bernoulli jump system based on the hidden Bernoulli model and the derived result. Finally, numerical simulations indicate that these proposed theories and methods are reliable and efficient.

This paper investigates the fuzzy adaptive practical tracking problem for a class of nonlinear pure-feedback systems with quantized input signal. In the control scheme design process, the considered system is transformed into a plant with a strict-feedback form by borrowing the mean value theorem of differential, then fuzzy logic systems are used to compensate for some uncertain nonlinearities in the considered plant and the classical adaptive technique is employed to handle some unknown parameters. In the backstepping design, some nonnegative switching functions are introduced to develop the desired fuzzy controller, and Barbalat's lemma is used to analyze the stability and the control performance of the closed-loop system. It can be shown that under the novel adaptive fuzzy controller, all the closed-loop signals are semiglobally uniformly ultimately bounded, and especially the tracking error satisfies the accuracy assigned a priori. A simulation example is presented to verify the effectiveness of the proposed control method.

The paper investigates the problem of l2-l∞ asynchronous static output feedback control for discrete-time Markov jump linear systems. The hidden Markov model is applied to describe the asynchronization between the original system and the designed controller. Via the system augmentation technique, the closed-loop system is represented as a descriptor system. Based on stochastic Lyapunov function technique, a sufficient condition is developed to ensure that the closed-loop system is stochastically admissible with a given l2-l∞ performance. By Finsler's lemma and the convexification approach, controller parameters can be obtained by solving a set of linear matrix inequalities. Finally, the applicability and effectiveness of the proposed approach is demonstrated by an example.

The problem of asynchronous dissipative control is investigated for Takagi-Sugeno fuzzy systems with Markov jump in this paper. Hidden Markov model is introduced to represent the nonsynchronization between the designed controller and the original system. By the fuzzy-basis-dependent and mode-dependent Lyapunov function, a sufficient condition is achieved such that the resulting closed-loop system is stochastically stable with a strictly ( , , )- -dissipative performance. The controller parameter is derived by applying MATLAB to solve a set of linear matrix inequalities. Finally, we present two examples to confirm the validity and correctness of our developed approach.

In this paper, we investigate the synchronization for heterogeneous network subject to event-triggering communication. The designed controller for each node includes reference generator (RG) and regulator. The predicted value of relative information between intermittent communication can significantly reduce the transmitted information. Based on the event triggering strategy and time-dependent threshold, all RGs can exponentially track the target trajectory. Then by the action of regulator, each node synchronizes with its RG. Meanwhile, a positive lower bound is obtained for the interevent intervals. Numerical example is given to demonstrate the effectiveness of the proposed event triggering strategy.

This paper deals with the problem of dissipativity-based asynchronous fault detection (FD) for Takagi-Sugeno fuzzy Markov jump systems with network data dropouts. It is assumed that data dropouts happen intermittently from the plant to the FD filter, which is described by Bernoulli process. The hidden Markov model is employed to describe the asynchronous phenomenon between the plant and filter. Based on Lyapunov theory, a sufficient condition is developed to guarantee that the FD system is stochastically stable with strictly dissipative performance. By choosing an appropriate Lyapunov function with the slack matrix technique and Finsler's Lemma, two approaches are proposed to compute filter gains by solving linear matrix inequalities. Finally, an example is provided to illustrate the usefulness and effectiveness of the proposed design methods.

This paper is concerned with the fuzzy-model-based nonfragile control problem for discrete-time nonlinear singularly perturbed systems with stochastic jumping parameters. The stochastic parameters are generated from the semi-Markov process. The memory property of the transition probabilities among subsystems is fully considered in the investigated systems. Consequently, the restriction that the transition probabilities are memoryless in widely used discrete-time Markov jump model can be removed. Based on the T-S fuzzy model approach and semi-Markov kernel concept, several criteria ensuring δ-error mean square stability of the underlying closed-loop system are established. With the help of those criteria, the designed procedures which could well deal with the fragility problem in the implementation of the proposed fuzzy-model-based controller are presented. A technique is developed to estimate the permissible maximum value of singularly perturbed parameter for discrete-time nonlinear semi-Markov jump singularly perturbed systems. Finally, the validity of the established theoretical results is illustrated by a numerical example and a modified tunnel diode circuit model.

This paper focuses on the dissipativity-based asynchronous filtering problem for a class of discrete-time Takagi-Sugeno fuzzy Markov jump systems subject to randomly occurred quantization. Considering the random fluctuations of network conditions, the randomly occurred quantization is introduced to describe the quantization phenomenon appearing in a probabilistic way. To take full advantage of the partial information of system modes for the desired system performance, we adopt the asynchronous filter in which mode transition matrix is nonhomogeneous. The mode-dependent time-varying delays are introduced, which have different bounds for different system modes. Via fuzzy-mode-dependent Lyapunov functional approach that can reduce conservatism, a sufficient condition on the existence of the asynchronous filter is derived such that the filtering error system is stochastically stable and strictly (Q, S, R)-dissipative. Then, the gains of the filter are obtained by solving a set of linear matrix inequalities (LMIs). An example is utilized to illustrate the validity of the developed filter design technique where the relationships among optimal dissipative performance indices, delays, quantization parameter, and the degree of asynchronous jumps are given.

This paper is concerned with the synchronization of complex networks subject to partial couplings and Takagi-Sugeno (T-S) fuzzy nodes, by adopting the aperiodic sampled-data strategy. A decoupling method is utilized to handle the partial couplings among connected nodes, which enables us to investigate each channel of complex networks independently. On the basis of the input-delay approach, the hybrid system about each channel is reformulated to a continuous time-varying delay system. Then, the free-weighting matrix approach and a novel continuous Lyapunov functional are adopted to capture the information of sampling pattern. Sufficient conditions are obtained to ensure that the complex networks achieves synchronization with the target node. Furthermore, the proposed strategy is extended to more general case, in which there exist constant transmission time delays in the local interactions of connected nodes. Based on Wirtinger's inequality, a simplified and efficient synchronization strategy is proposed. Moreover, the corresponding optimization problem about the maximal sampling interval upper bound is addressed as well. Finally, the communication frequency reduction potential of the proposed synchronization strategy is well demonstrated via a numerical example.

In this brief, we study the delayed feedback stabilization problem for Boolean control networks (BCNs) with state delay. Using the semi-tensor product of matrices, some necessary and sufficient conditions are obtained. For the stabilization of BCNs, detailed procedure to construct the feedback controllers is also presented. We further derive the number of different feedback controllers, which can successfully stabilize the BCN in a finite time. Finally, an illustrative example is presented to show the effectiveness of our method.

This paper is concerned with the exponential synchronization issue of general chaotic neural networks subject to nonuniform sampling and control packet missing in the frame of the zero-input strategy. Based on this strategy, we make use of the switched system model to describe the synchronization error system. First, when the missing of control packet does not occur, an exponential stability criterion with less conservatism is established for the resultant synchronization error systems via a superior time-dependent Lyapunov functional and the convex optimization approach. The characteristics induced by nonuniform sampling can be used to the full because of the structure and property of the constructed Lyapunov functional, that is not necessary to be positive definite except sampling times. Then, a criterion is obtained to guarantee that the general chaotic neural networks are synchronous exponentially when the missing of control packet occurs by means of the average dwell-time technique. An explicit expression of the sampled-data static output feedback controller is also gained. Finally, the effectiveness of the proposed new design methods is shown via two examples.

The problem of dissipativity-based resilient filtering for discrete-time periodic Markov jump neural networks in the presence of quantized measurements is investigated in this paper. Due to the limited capacities of network medium, a logarithmic quantizer is applied to the underlying systems. Considering the fact that the filter is realized through a network, randomly occurring parameter uncertainties of the filter are modeled by two mode-dependent Bernoulli processes. By establishing the mode-dependent periodic Lyapunov function, sufficient conditions are given to ensure the stability and dissipativity of the filtering error system. The filter parameters are derived via solving a set of linear matrix inequalities. The merits and validity of the proposed design techniques are verified by a simulation example.

This paper considers the nonfragile $H_\infty $ estimation problem for a class of complex networks with switching topologies and quantization effects. The network architecture is assumed to be dynamic and evolves with time according to a random process subject to a sojourn probability. The coupled signal is to be quantized before transmission due to power and bandwidth constraints, and the quantization errors are transformed into sector-bounded uncertainties. The concept of nonfragility is introduced by inserting randomly occurred uncertainties into the estimator parameters to cope with the unavoidable small gain variations emerging from the implementations of estimators. Both the quantizers and the estimators have several operation modes depending on the switching signal of the underlying network structure. A sufficient condition is provided via a linear matrix inequality approach to ensure the estimation error dynamic to be stochastically stable in the absence of external disturbances, and the $H_\infty $ performance with a prescribed index is also satisfied. Finally, a numerical example is presented to clarify the validity of the proposed method.

This paper investigates the global pinning synchronization problem for a class of complex networks with aperiodic samplings. Combined with the Writinger-based integral inequality, a new less conservative criterion is presented to guarantee the global pinning synchronization of the complex network. Furthermore, a novel condition is proposed under which the complex network is globally pinning synchronized with a given performance index. It is shown that the performance index has a positive correlation with the upper bound of the sampling intervals. Finally, the validity and the advantage of the theoretic results obtained are verified by means of the applications in Chua's circuit and pendulum.

This paper is concerned with the nonfragileH∞control problem for discrete-time fast sampling Markovian jump singularly perturbed nonlinear systems described by the Takagi-Sugeno fuzzy model. By utilizing singular perturbation theory, a singular perturbation parameter (SPP) independent, i.e., ε-independent, condition is derived to make sure the underlying closed-loop system's stability and a mixedH∞and passive performance γ, simultaneously. The ill-conditioned case caused by SPP could be eliminated on the basis of such a condition. With the aid of the stochastic analysis approach, the desired controller gains can be obtained, where the nonfragile property is fully considered to improve the tolerance of controller. Furthermore, a technique is developed to estimate the upper bound of SPP ε in this paper by employing a useful inequality. The availability and practicability of the proposed design method are finally explained via a practical example of a tunnel diode circuit with a modified model and a numerical example.

The problem of asynchronous filtering of nonlinear systems is investigated in this paper. To facilitate analysis, a discrete-time Takagi-Sugeno fuzzy model with a fast and uniform period is introduced to approximate continuous nonlinear systems. A slow and nonuniform sampler between system and filter is proposed to overcome the contradictions between fast sampling period and limited bandwidth. The nonuniform sampling interval of sampler is subject to a Markov chain. To make full use of the partial information of sampling interval which is accessible to the filter, the asynchronous filter which is described by the hidden Markov model is introduced to reduce conservatism. By resorting to the augmentation approach and the Lyapunov functional method which depends on nonuniform sampling interval, some sufficient conditions for the existence of asynchronous filer are given to guarantee the stability and dissipativity of the augmented system. The gains of asynchronous filter are given via solving a set of linear matrix inequalities. Two examples are utilized to illustrate the validity of the developed filter design technique where the relationships between optimal dissipative performance indices and the mode synchronization rate are given.

In this paper, the leader-following consensus problem of high-order multiagent systems via event-triggered control is discussed. A novel distributed event-triggered communication protocol based on state estimates of neighboring agents is proposed to solve the consensus problem of the leader-following systems. We first investigate the consensus problem in a fixed topology, and then extend to the switching topologies. State estimates in fixed topology are only updated when the trigger condition is satisfied. However, state estimates in switching topologies are renewed with two cases: 1) the communication topology is switched or 2) the trigger condition is satisfied. Clearly, compared to continuous-time interaction, this protocol can greatly reduce the communication load of multiagent networks. Besides, the event-triggering function is constructed based on the local information and a new event-triggered rule is given. Moreover, “Zeno behavior” can be excluded. Finally, we give two examples to validate the feasibility and efficiency of our approach.

In this paper, the problem of event-based synchronization of heterogeneous complex networks is investigated. Specifically, the influence of transmission delays on event-based synchronization is considered. The designed distributed controller for each nonidentical node in heterogeneous network includes reference generator (RG) and robust regulator. Event-based communication protocol is utilized to ensure the synchronization among the identical RGs and target leader. Meanwhile, the interevent intervals are lower bounded by a positive constant. Furthermore, the proposed high-gain output feedback and adaptive control law guarantee that all nonidentical nodes converge to their RGs. Based on these two steps, which are coincided with the separation principle, nonidentical nodes can track the target leader. In addition, theoretical results are demonstrated by a numerical example.

In this paper, the problem of distributed event-triggered pinning control for practical consensus of multiagent systems (MASs) with quantized communication based on a directed graph is investigated. The pinning control for practical consensus of MASs with uniform quantizer is first discussed. Then, in order to decrease communication load of interagent, the event-triggered quantized communication protocol is designed. The nonsmooth analysis and Gronwall's inequality approach is used to guarantee the existence of a solution to the resulting closed-loop system. It is shown that practical consensus is reachable through the event-triggered control and converges to a consensus set. Moreover, “Zeno phenomenon” can be excluded. Finally, an example is given to validate the feasibility and efficiency of the proposed new design method.

In this paper, we study the state estimation and optimal control [i.e., linear quadratic Gaussian (LQG) control] problems for networked control systems in which control inputs, observations, and packet acknowledgments (ACKs) are randomly lost. The packet ACK is a signal that is transmitted from the actuator to notice the estimator the occurence of control packet loss. For such systems, we obtain the optimal estimator, which is consisted of exponentially increasing terms. For the solvability of the LQG problem, we come to a conclusion that in general even the optimal LQG control exists, it is impossible and unnecessary to be obtained as its calculation is not only technically difficult but also computationally prohibitive. This issue motivates us to design a suboptimal LQG controller for the underlying systems. We first develop a suboptimal estimator by using the estimator gain in each term of the optimal estimator. Then we derive a suboptimal LQG controller and establish the conditions for stability of the closed-loop systems. Examples are given to illustrate the effectiveness and advantages of the proposed design scheme.

This paper is concerned with the problem of reliable dissipative control for Takagi-Sugeno fuzzy systems with Markov jumping parameters. Considering the influence of actuator faults, a sufficient condition is developed to ensure that the resultant closed-loop system is stochastically stable and strictly ( Q, S,R )-dissipative based on a relaxed approach in which mode-dependent and fuzzy-basis-dependent Lyapunov functions are employed. Then a reliable dissipative control for fuzzy Markov jump systems is designed, with sufficient condition proposed for the existence of guaranteed stability and dissipativity controller. The effectiveness and potential of the obtained design method is verified by two simulation examples.

In this paper, the problem of output synchronization is investigated for the heterogeneous network with an uncertain leader. It is assumed that parameter perturbations influence the nonidentical linear agents, whose outputs are controlled to track the output of an uncertain leader. Based on the hierarchical structure of the communication graph, a novel control scheme is proposed to guarantee the output synchronization. As there exist parameter uncertainties in the models of the agents, the internal model principle is used to gain robustness versus plant parameter uncertainties. Furthermore, as the precise model of the leader is also not available, the adaptive control principle is adopted to tune the parameters in the local controllers. The developed new technique is able to simultaneously handle uncertainties in the follower parameters as well as the leader parameters. The agents in the upper layers will be treated as the exosystems of the agents in the lower layers. The local controllers are constructed in a sequential order. It is shown that the output synchronization can be achieved globally asymptotically and locally exponentially. Finally, a simulation example is given to illustrate the effectiveness and potential of the theoretic results obtained.

In this paper, the reachable set estimation problem is investigated for Markovian jump neural networks (NNs) with time-varying delays and bounded peak disturbances. Our goal is to find a set as small as possible which bounds all the state trajectories of the NNs under zero initial conditions. In the framework of Lyapunov-Krasovskii theorem, a newly-found summation inequality combined with the reciprocally convex approach is used to bound the difference of the proposed Lyapunov functional. A new less conservative condition dependent on the upper bound, the lower bound and the delay range of the time delay is established to guarantee that the state trajectories are bounded within an ellipsoid-like set. Then the result is extended to the case with incomplete transition probabilities and a more general condition is derived. Finally, examples including a genetic regulatory network are given to demonstrate the usefulness and the effectiveness of the results obtained in this paper.

The paper is concerned with the H∞and I2-I∞filtering design problems for discrete-time nonlinear switched systems with quantized measurements using the Takagi-Sugeno (T-S) fuzzy model. The systems under consideration inherently combine features of the switched hybrid systems and the T-S fuzzy systems. The sector bound approach is employed to deal with quantization effects. Based on the fuzzy-basis-dependent Lyapunov function, sufficient conditions are established such that the filtering error system is stochastically stable and a prescribed noise attenuation level in an H∞or I2-I∞sense is achieved. Both numerical and practical examples are provided to show the feasibility and efficiency of the design schemes.

This paper is concerned with the problem of reliable switched controller design for a class of discrete-time Takagi-Sugeno fuzzy systems with randomly occurring infinitedistributed delays and quantization as well as actuator failures. A random Bernoulli process is used to describe the stochastic infinite-distributed delays. Due to limited communication capacity, the control signal is quantized before being transmitted to the actuator by the logarithmic quantizer. We apply the switching mechanisms to categorize the stochastic behavior of actuator faults. Based on the parallel distributed compensation, the switched feedback controller is designed. By the fuzzy-basisdependent Lyapunov functional approach, sufficient conditions are obtained to ensure that the resulting closed-loop system is exponentially stable in the mean-square sense with a given l2-l∞performance index. Then, a numerical example is presented to demonstrate the effectiveness of the proposed new design method.

This paper investigates the problem of nonfragile guaranteed cost control for discrete-time Takagi-Sugeno fuzzy Markov jump systems with time-varying delays. With the help of the parallel distributed compensation, a nonfragile fuzzy controller is designed. Then via Lyapunov-Krasovskii functional approach, sufficient conditions are obtained ensuring that the resulting closed-loop system is asymptotically stable with an upper bound of the guaranteed cost index. The optimal upper bound of the guaranteed cost index and the controller gain can be achieved via the optimization technique. Finally, an example is presented to show the effectiveness of the proposed new design techniques.

This paper is concerned with the consensus of multiagent systems with nonlinear dynamics through the use of aperiodic sampled-data controllers, which are more flexible than classical periodic sampled-data controllers. By input delay approach, the resulting sampled-data system is reformulated as a continuous system with time-varying delay in the control input. A continuous Lyapunov functional, which captures the information on sampling pattern, together with the free-weighting matrix method, is then used to establish a sufficient condition for consensusability. For a more general case that the sampled-data controllers are subject to constant input delays, a novel discontinuous Lyapunov functional is introduced on the basis of the vector extension of Wirtinger's inequality. This functional can lead to simplified and efficient stability conditions for computation and optimization. Further results on the estimate of maximal allowable sampling interval upper bound is given as well. Numerical example is provided to show the effectiveness and merits of the proposed protocol.

The distributed asynchronous passive filtering problem is studied for nonlinear Markov jump systems subject to packet loss. In the distributed case, the asynchronous phenomenon between the filters and system, which is more complicated than the common ones, is handled by introducing a mutually independent Markov chain. A sufficient condition for passive performance is proposed for the filtering error system via a mode-dependent Lyapunov functional, based on which the filter design scheme is also outlined by explicitly characterizing the filter parameters in terms of matrix inequalities.

This paper is concerned with the problem of dissipative control for Takagi-Sugeno fuzzy systems under time-varying sampling with a known upper bound on the sampling intervals. Based on the time-dependent Lyapunov-Krasovskii functional approach, which makes full use of the available information about the actual sampling pattern, a sufficient condition is established to guarantee the sampled-data systems to be exponentially stable and strictly (Q, S, R)-γ-dissipative. Based on the criterion, a design algorithm for the desired sampled-data controller is proposed. The effectiveness and benefits of the results developed in this paper is demonstrated by a controller design for a truck-trailer system.

This study is concerned with the problem of the elimination of overflow oscillations (EOOs) for two‐dimensional (2D) digital filters with external interference. The main purpose is the presentation of a new unified criterion such that the underlying 2D digital filter is stable with a positive prescribed interference attenuation level. A performance index is proposed for the first time, which is referred to as generalised dissipativity property. By using this index and two harmonic slack matrices, a novel criterion is established, which can be used to solve ℋ ∞ EOOs, passive EOOs and l 2 – l ∞ EOOs for 2D digital filters with external interference in a unified framework, and reduce the conservatism of the existing results. The effectiveness of the criterion is demonstrated by a numerical example.

This paper investigates the problem of local synchronization of chaotic neural networks with sampled-data and actuator saturation. A new time-dependent Lyapunov functional is proposed for the synchronization error systems. The advantage of the constructed Lyapunov functional lies in the fact that it is positive definite at sampling times but not necessarily between sampling times, and makes full use of the available information about the actual sampling pattern. A local stability condition of the synchronization error systems is derived, based on which a sampled-data controller with respect to the actuator saturation is designed to ensure that the master neural networks and slave neural networks are locally asymptotically synchronous. Two optimization problems are provided to compute the desired sampled-data controller with the aim of enlarging the set of admissible initial conditions or the admissible sampling upper bound ensuring the local synchronization of the considered chaotic neural networks. A numerical example is used to demonstrate the effectiveness of the proposed design technique.

In this paper, a sampled-data fuzzy controller is designed to stabilize a class of chaotic systems. A Takagi-Sugeno (T-S) fuzzy model is employed to represent the chaotic systems. Based on this general model, the exponential stability issue of the closed-loop systems with an input constraint is first investigated by a novel time-dependent Lyapunov functional, which is positive definite at sampling times but not necessary between the sampling times. Then, two sufficient conditions are developed for sampled-data fuzzy controller synthesis of the underlying T-S fuzzy model with or without input constraint. All the proposed results in this paper depend on both the upper and lower bounds on a sampling interval, and the available information about the actual sampling pattern is fully utilized. The proposed sampled-data fuzzy control scheme is successfully applied to the chaotic Lorenz system, which is shown to be effective and less conservative compared with existing results.

This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques.

In this paper, the problem of sampled-data synchronization for Markovian jump neural networks with time-varying delay and variable samplings is considered. In the framework of the input delay approach and the linear matrix inequality technique, two delay-dependent criteria are derived to ensure the stochastic stability of the error systems, and thus, the master systems stochastically synchronize with the slave systems. The desired mode-independent controller is designed, which depends upon the maximum sampling interval. The effectiveness and potential of the obtained results is verified by two simulation examples.

This paper investigates the problem of robust passive control for networked fuzzy systems, where randomly occurring uncertainties, variable sampling intervals, and constant network-induced delay are taken into account. A discontinuous Lyapunov functional is introduced for the closed-loop systems, which takes full advantage of the sawtooth structure of the time-varying interval delay induced by sample-and-hold and signal transmission. A sufficient condition is proposed to ensure the closed-loop system to be robustly stochastically passive. Then, the problem of robust passive control is solved. Two examples are utilized to illustrate the advantages and effectiveness of the results that are proposed in this paper.

In this paper, the problem of dissipativity analysis is discussed for discrete-time stochastic neural networks with time-varying discrete and finite-distributed delays. The discretized Jensen inequality and lower bounds lemma are adopted to deal with the involved finite sum quadratic terms, and a sufficient condition is derived to ensure the considered neural networks to be globally asymptotically stable in the mean square and strictly (Q, S, R)-y-dissipative, which is delay-dependent in the sense that it depends on not only the discrete delay but also the finite-distributed delay. Based on the dissipativity criterion, some special cases are also discussed. Compared with the existing ones, the merit of the proposed results in this paper lies in their reduced conservatism and less decision variables. Three examples are given to illustrate the effectiveness and benefits of our theoretical results.

This paper studies the problem of sampled-data control for master-slave synchronization schemes that consist of identical chaotic Lur'e systems with time delays. It is assumed that the sampling periods are arbitrarily varying but bounded. In order to take full advantage of the available information about the actual sampling pattern, a novel Lyapunov functional is proposed, which is positive definite at sampling times but not necessarily positive definite inside the sampling intervals. Based on the Lyapunov functional, an exponential synchronization criterion is derived by analyzing the corresponding synchronization error systems. The desired sampled-data controller is designed by a linear matrix inequality approach. The effectiveness and reduced conservatism of the developed results are demonstrated by the numerical simulations of Chua's circuit and neural network.

This paper studies the problem of sampled-data exponential synchronization of complex dynamical networks (CDNs) with time-varying coupling delay and uncertain sampling. By combining the time-dependent Lyapunov functional approach and convex combination technique, a criterion is derived to ensure the exponential stability of the error dynamics, which fully utilizes the available information about the actual sampling pattern. Based on the derived condition, the design method of the desired sampled-data controllers is proposed to make the CDNs exponentially synchronized and obtain a lower-bound estimation of the largest sampling interval. Simulation examples demonstrate that the presented method can significantly reduce the conservatism of the existing results, and lead to wider applications.

In this paper, the problem of reliable$H_\infty$control is investigated for discrete-time Takagi–Sugeno (T–S) fuzzy systems with infinite-distributed delay and actuator faults. A discrete-time homogeneous Markov chain is used to represent the stochastic behavior of actuator faults. In terms of a stochastic fuzzy Lyapunov functional, a sufficient condition is proposed to ensure that the resultant closed-loop system is exponentially stable in the mean-square sense with an$H_\infty$performance index. Based on the derived condition, the reliable$H_\infty$control problem is solved, and an explicit expression of the desired controller is also given. The case of no failure in the actuator is also considered. A numerical example is given to demonstrate that our results are effective and less conservative.

This paper is concerned with the problems of stability and dissipativity analysis for static neural networks (NNs) with time delay. Some improved delay-dependent stability criteria are established for static NNs with time-varying or time-invariant delay using the delay partitioning technique. Based on these criteria, several delay-dependent sufficient conditions are given to guarantee the dissipativity of static NNs with time delay. All the given results in this paper are not only dependent upon the time delay but also upon the number of delay partitions. Some examples are given to illustrate the effectiveness and reduced conservatism of the proposed results.

This paper investigates the problem of master-slave synchronization for neural networks with discrete and distributed delays under variable sampling with a known upper bound on the sampling intervals. An improved method is proposed, which captures the characteristic of sampled-data systems. Some delay-dependent criteria are derived to ensure the exponential stability of the error systems, and thus the master systems synchronize with the slave systems. The desired sampled-data controller can be achieved by solving a set of linear matrix inequalitys, which depend upon the maximum sampling interval and the decay rate. The obtained conditions not only have less conservatism but also have less decision variables than existing results. Simulation results are given to show the effectiveness and benefits of the proposed methods.

In this paper, passivity analysis is conducted for discrete-time stochastic neural networks with both Markovian jumping parameters and mixed time delays. The mixed time delays consist of both discrete and distributed delays. The Markov chain in the underlying neural networks is finite piecewise homogeneous. By introducing a Lyapunov functional that accounts for the mixed time delays, a delay-dependent passivity condition is derived in terms of the linear matrix inequality approach. The case of Markov chain with partially unknown transition probabilities is also considered. All the results presented depend upon not only discrete delay but also distributed delay. A numerical example is included to demonstrate the effectiveness of the proposed methods.

In this paper, the problem of stability analysis is investigated for switched neural networks with time-varying delay using linear matrix inequality (LMI) approach. By taking advantage of the average dwell time method, two sufficient conditions are developed to ensure the global exponential stability of the considered neural networks, which are delay-dependent and formulated by LMIs. The state decay estimate is explicitly given. Numerical examples are provided to demonstrate the effectiveness and feasibility of the proposed techniques.

This brief investigates the problem of global exponential stability analysis for discrete recurrent neural networks with time-varying delays. In terms of linear matrix inequality (LMI) approach, a novel delay-dependent stability criterion is established for the considered recurrent neural networks via a new Lyapunov function. The obtained condition has less conservativeness and less number of variables than the existing ones. Numerical example is given to demonstrate the effectiveness of the proposed method.